{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from fastai import *\n",
    "from fastai.vision import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据下载地址：http://www.cs.toronto.edu/~nitish/unsupervised_video/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.load('./mnist_test_seq.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 10000, 64, 64)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape #20张图片，10000个sample，1通道的64 * 64图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": "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\n",
      "image/png": "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\n",
      "text/plain": [
       "Image (1, 64, 64)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(torch.from_numpy(data[0, 0, :, :]).unsqueeze(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": "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\n",
      "image/png": "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\n",
      "text/plain": [
       "Image (1, 64, 64)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(torch.from_numpy(data[1, 0, :, :]).unsqueeze(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "from npy: 160000it [00:10, 14711.56it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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887S1nNaiFpbVJj3x6FrPRPV1lbOTUJyhbE8t/JxIqm29JsAMeHkpNy4dQjPNrExN3Lv7NSxsWsKkz59O0yOP0/TI46uPfX7CML417EHuXZ7Y9JwBq4NNX9DWclqLWlhW+8pP/rXdxu5as/R7Iwv1Ef9yG/XDhrV7Ti38nEiqbb0mwKT7HmbKY4evrptSM3v9YRL9b7y3cNySj+7NfWf9EIDjb/0sadacbh1nd6h8wap8saobNIimimW1iU8eythz3pl9emdZDfa87iw2z3RZbchTTR3ORLWmL/d71FX8b8oWf4NNB7d9bI38nEiqbb2mB6ZSHcGn/34g7/n8I6t7XgAaRm/LUef+iToCgB/sdw0/ZXSrj5G7unK2nLLF35iw6THULV3KwqtHcu9O1/C3FaVlNYDmJaVlgfodxrL7Lx4tLKs1t/novdug6XcD8IV//xi/Gvdrjt74Zb5z5vG8+6K21/z7cr9Hy+wkwOEDX2dlavvYukGDaubnpK/5yk/+tVDPOvO/C/Whky4o1PulLxTq1vrleruGfyj2c7X8f7jFgQNWFupDtjyleP5c1kn1bO5/fvZ/C/VhGy0t1PPHFmsgq98dfV2vmYEBOHyruTRQTzOJS7b5I2/vvyMr/2mP1f8s/Uni7M3mrT7+G3/7cA+OtutUL6cBvHbMLjW3rNbWTFS1vt7v0TI7WTlD+chZQ1s9thZ/TiTVpl4VYCRJktZGr1pCOvVddwMDaYx6mlLwh//9aavHPbpyOUff9VnGfGsZTd07xG7R8hf34btdBZT+2j7hA7et7gsa12ZfUHD8rZ9l3KxZPTDqrlG9lMYrr6xxTKlh9dLV9Rm//hTbPXPX6rq6YXXzPtCwOnrsS9QNHEjz0nemuBu23oqDv1CbPyeSak+vCjAH/OKLzPnkxTSnRDPNXP7mtpwy5LnCMUfMO4pVk7fgH269v0+Gl9Yc8/5ZfGvYg3z67wey/alzqGyB6Mt9QS1Lae31fdQNGlST9wv63Q7TOWz/z9HvllIIadjy3bzn+hdr8uekrxjyVPE32lMri/0fLfcCalHd3L629wfqSdFQfMlZ8pMo1NU9LzcvG1ioK5eGAVZ1cL2GLYv3jfnGKVcW6uqel5ebinVry82b0Td+h/QFvSrAjP3fV+CT79QPLN4GKgLMYY/+M43HLqH+1ft7YHQ953vvnsX8pqXMvWwnNtl/RWHfii8vXKMvaGv6xjuzWpuJGve5p1bvr8WG1TrqaIz6UqCLd37573nzc5wz9KGa/DmRVJt6VYChqfTW2TGNxTtQrk7h39ycplefa+XEvqnlxaopNbNl/UDu/taP2jy2ry+rwZrLJqWG1R+XG1bPpP8jxSWT5ycM47fDru4TDauvzyk17Tbv1szKBM0088pujYy8ubS/ZfnVnxNJtaJXBZimJ55mwu/P4LEj/geAqSNvZ/aKVfzoiCMBqJs3uyeH161enzO08GI1ZeFuDG1YTGOsqtlltcplk1rr9xj7v+Wp84oZyn2PeYBn/rP09T/9+EukBpj4sZk1/3MiqTb0qgADQEBj1AP1LGxawtmnnkG/eXm/+KyP6uW0l5Zvwl27NlK/xXBOub+2ltWql01qst+jqRQ5WpuhBNjqO6V7gNx1XiPwrpr8Ocldy/2PWnzh3z9WqH81rniPlKM3frlQf+fM4wt1e/dN6imr9t+lUFc237fmjF9/qlBXNuevjbnnbVWoPzro9UL9VvPbhfqoycWel77Q8N+X9b4Ak2BlaqKZZr42/59WNynWnFaW0+oGD+ataRsDtbOsVj0T9cpujXz0u7XX79H0xNMAhRnKttTiz4mk2tO7AkxdPfvu/Dh1BM3AmcP/yCkf/Y81/jKpBdXLaQBvXDOc2957bU0tq1XPRO17zAO13e9RnqFcmaA+1nxrVt3gwTX5cyKp9vSqABO7vodpo37GwytXsH1jPaMbG3l7szoG9fTAekrVi9Wdu05nYdOy2lpWa2Umqqb7PSpmKJtSrLH7sck78eSu/1N7PyeSak6vCjD1r73FjGUb8e/Xn8YvP3YR2zfW9/SQelbFi9X57/4T9y1vYOINZzH2ltqZkWptJqqW+z12uOhNOKL0dfUM5cLPfIBZH7ugJn9O+qLqTyGn/XYRjviX2wr17Cu3KNRNrdwEsqvVDSr++dlyz6a2tNzLqUXlh5ACHd4GoX6TTQp1y4eWvqN4X5k9rzurUPeV+0TVil4VYFY9+xwXjtmB7biLL569DwCb1/BNgypfrDapG8CXnvwIY0+vwReldpZNaq3fo2nuYzyycuWaM5R19ezwqUdq++dEUk3xs5B6sZYXqwbqWUUTb149suOT+qI2lk1a+j1mvve61f0ePzriSOpu69v9Hmd9+jTmrSwujJWWX//E/7w5iv6f79dDI5Ok7mOA6eXO+vRpPLxyRccH9mE7XPTm6q/PHP5Hlnx0b6DU73HnrtN5vXkZZ596Gk3znqBp3hM9NcxuU//n+/nitvvw4ZF7sfklpRnKluXXC393RE18DySpVy0hSZJaV9fB35tTtvhboZ6w6THFA7qhB6a652Vh1azxvTtdU6j/tqL4x1nLx4G0aPlYkLbU7zC2UO/+i0cL9bYNxZ6XfR88tlCP+/q69diodzHA9HItf21D7fYDtdb3scyG1YLK/jFJqgUuISkL1X0f7zSsfsyGVUmqQc7AKAuVM1FD3/cW0859p2G1T93nRZK0Vgwwyk5lw+p281wyUd804OWlhfrGpUMK9eEDi5/rs3LNGzN3u9eOKX7W0b27/7hQL2wqfiL8pM+fWairP1G+I89PGFaofzvs6uL1lxe/KZueM6BQ5/wJ9TLAKEP2e0iS7IGRJEnZaXcGZkbztWt+2IokSVIPcwZGkiRlxx4YSeqF0n0PF+opjx1eqA/f7ap2z3/krKGFetznnuqcgVVo2HqrQn3wF4ofKNmUireG2+sPk4pjunHdmnZb7sLd4r6zflh1RHHR4PhbP1u83iw/nb0vcQZGkiRlxwAjSZKyY4CRJEnZsQdGkvqg0WNfKtR1A4sfbNi8tHijvLXRsOW7C/V7rn+xUH9r2IOF+tN/P7BQb3/qnELd0b33GkZvW6iPOvdPhbqO9t8o+4P9ih8e+VNGd3BF5cQZGEmSlB0DjCRJyo4BRpIkZcceGEnqg363w/RCfdj+nyvU/W5Z93ui7Hnzc4X6nKEPFer5TcW+mrmX7VSoN9l/xTpdb8WXFxbqszebt07nf+NvHy7UWzOnjSOVI2dgJElSdgwwkiQpOwYYSZKUHXtgJClDdVV/fzZGfaFeWX2TlWj/nilr49R33V21pXhvmS3ri/Xd3/rRBl+zPY+uXF6oj76r+NlHY761rFA3delo1N2cgZEkSdkxwEiSpOwYYCRJUnbsgZGkDLw+Z2ihbt6tuVBX97w0U9z/ym6NhXrkzes+hn/68ZcKdap6BZn4sZmFemjD4kLdGKsK9SlDiveV6cgR844q1Ksmb1Go/+HW+wu1PS99mzMwkiQpOwYYSZKUHQOMJEnKTqRUfbMASepZB9dN9BdTlfrtxxTqX/3pF4W6+r4w1T0wk144oFA/8/7iPVK6Q/0Wwwv1b+9vvxHnsEf/uVA3HrukUDe9+lrnDEy92ozma1u9iZEzMJIkKTsGGEmSlB0DjCRJyo73gZGkHDQV72ry1MqVhXpMY//uHM1aqRs8uFC/NW3jdo+/eVnxs5T45uaFsunVdbtvjPo2Z2AkSVJ2DDCSJCk7BhhJkpQde2AkKQNNTzxdqCf8/oxC/dgR/9Odw2lVdc/LG9cU7/ty23uvLdSzVxQ/G+lHRxxZfLx5sztxdOprnIGRJEnZMcBIkqTsGGAkSVJ27IGRpBxVfTpMY9QX6pVVnyZVH13/8VKPTd6pUD+5a7EvZ2FT8fOXzj612MfTb96srhmY+iRnYCRJUnYMMJIkKTsGGEmSlB17YCQpR1UtLStT8bOSmmku1E2pqmmmEyz8zAcK9ayPXVCo71tefImZeMNZhXrsLXd3+phUO5yBkSRJ2THASJKk7BhgJElSduyBkaQM7XDRm8UNR7R//JnD/1ioT/nofxTqQdPXoh+lrnivmR0+9Uih3qRuQKH+0pMfKdRjT7fnRZ3HGRhJkpQdA4wkScqOAUaSJGXHHhhJylDT3McK9SMrVxbq7RuL/SqjGxsL9dubFf9+HbQW14xd31Oop436WaH+nzdHFer+n+9XqIt3qpE2jDMwkiQpOwYYSZKUHQOMJEnKjj0wktQHnPXp0wr1f10+tVBX98Ssj/rX3irUM5ZtVKgv/F3xZjTbzbtrg68ptcUZGEmSlB0DjCRJyo4BRpIkZSdSSj09BkkqOLhuor+YJAEwo/naaG27MzCSJCk7BhhJkpQdA4wkScqOAUaSJGXHACNJkrJjgJEkSdkxwEiSpOwYYCRJUnYMMJIkKTsGGEmSlB0DjCRJyo4BRpIkZccAI0mSsmOAkSRJ2THASJKk7BhgJElSdgwwkiQpOwYYSZKUHQOMJEnKjgFGkiRlxwAjSZKyY4CRJEnZMcBIkqTsGGAkSVJ2DDCSJCk7BhhJkpQdA4wkScqOAUaSJGXHACNJkrJjgJEkSdkxwEiSpOwYYCRJUnYMMJIkKTsGGEmSlB0DjCRJyo4BRpIkZccAI0mSsmOAkSRJ2THASJKk7BhgJElSdiKl1NNjkCRJWifOwEiSpOwYYCRJUnYMMJIkKTsGGEmSlB0DjCRJyo4BRpIkZccAI0mSsmOAkSRJ2THASJKk7BhgJElSdgwwkiQpOwYYSZKUHQOMJKnTRcT+ETGvp8ehvssAI0k1JCKeiYhlEbE4IhZExBURsXFnXyel9NeU0vad/bjVIqJfRCxs7TmUn+vLETGoYtvJEXFrJ1z33Ij4+YY+jtafAUaSas9RKaWNgd2BPYGvVx8QJTm8RhwAzE4pLW5jfz1wRjeOR90khx9OSVIXSCm9ANwE7AwQEbdGxPkRcTuwFBgdEUMi4rKImB8RL0TEtyOiPiL6R8QbEbFzy+NFxLDy7M7wiDgwIp6v2DciIqZHxCsR8XRETCpvH1A+Z2i5/lpErIqITcr1eRFxUTtPYwLwu3b2fx84OyI2bW1nRLwnImZExGsRMS8iji1v7xcRsyPi9HJdHxG3R8Q5EXEY8FXguPJM1oPtf6fVFQwwklSjImJrSgHggYrNJwKfAQYDzwJXAKuAMcD7gEOAk1NKy4FfAsdXnHssMDOl9HLVdeqA3wIPAiOBg4AzI+LQlNLbwL3A+PLh48vX3beintnO05gA3NjO/lnArcDZ1TvKS0szgKuA4cDHgakRsWNKaQXwSWByROwAfIXSbM75KaWbgSnA1SmljVNKu7ZzfXURA4wk1Z7rI+IN4DZK4WBKxb4rUkoPp5RWAZtRCghnppSWlIPJhZRe6KH0wv/xinM/Ud5WbS9gWEppckppRUrpKeCSinNnAuMjogHYBbi4XA8on/uX1p5ERGwHNKSUOmoWPgc4PSKGVW0/EngmpXR5SmlVSukBYDowESClNAf4NnA9pQB0YkqpqYNrqZs09PQAJEnd7iMppT+0se+5iq9HAY3A/Iho2VZXccyfgYERsTewANgN+FUrjzkKGFEOTS3qgb+Wv54J/BelnpyHKM2KXAbsAzyRUnq1jbFOoLQE1q6U0pyIuIHSLMojVePau2pcDcDPKuppwPnA9JTS4x1dS93HACNJqpQqvn4OWA4MLc/IFA9MqSkirqG0jLQAuCGltKiVx3wOeDqlNLaNa94BbA8cTWkJam5EbEMpoHS0fHRxR0+o7JvA/cAFVeOamVI6uJ3zpgI3AIdGxH4ppdvK21M756gbuIQkSWpVSmk+8HvggojYJCLqImK7iBhfcdhVwHHACbS+fARwD7AoIr4cERuVG2J3joi9ytdZCtwHfJ53AssdwKm0EWAiYiDwfkqzQGvzXJ4ArgYmVWy+ARgXESdGRGP5n73KPS9ExInAHsCnyudNq3i79gJg20zeqdUn+Y2XJLXnJKAfMBd4HbgO2LJlZ0rpbmAJMII2lnPKfSNHUlpiehpYCFwKDKk4bCal5ap7KurBtNH/AnwIuLPcBLy2JgOr7wlTni06hFIvzovAS0yWl0YAACAASURBVMB3gf7lGaCLgJNSSotTSldRagi+sHz6teV/vxoR96/DGNRJIiVnwSRJeYmIqcCclNLUnh6LeoY9MJKkHM2m9NZs1ShnYCRJUnbsgZEkSdlxCUlSr3Nw3USnhiUBMKP52mhtuzMwkiQpOwYYSZKUHQOMJEnKjgFGkiRlxwAjSZKyY4CRJEnZMcBIkqTsGGAkSVJ2DDCSJCk7BhhJkpQdA4wkScqOAUaSJGXHACNJkrJjgJEkSdkxwEiSpOwYYCRJUnYMMJIkKTsGGEmSlB0DjCRJyo4BRpIkZccAI0mSsmOAkSRJ2THASJKk7BhgJElSdgwwkiQpOwYYSZKUHQOMJEnKjgFGkiRlxwAjSZKyY4CRJEnZMcBIkqTsGGAkSVJ2DDCSJCk7BhhJkpQdA4wkScqOAUaSJGXHACNJkrJjgJEkSdkxwEiSpOwYYCRJUnYMMJIkKTsGGEmSlB0DjCRJyo4BRpIkZccAI0mSsmOAkSRJ2THASJKk7BhgJElSdgwwkiQpOwYYSZKUHQOMJEnKjgFGkiRlxwAjSZKyY4CRJEnZMcBIkqTsGGAkSVJ2DDCSJCk7BhhJkpQdA4wkScqOAUaSJGXHACNJkrJjgJEkSdkxwEiSpOwYYCRJUnYMMJIkKTsGGEmSlB0DjCRJyk5DTw9AkrThoqH467zpA+8t1Kk+CvXTJ6d1vsaBYx4v1Nts9Fqh/vrQOYW6Pop/I9+8tH+h/uFBhxTqVc8+t85jUu1yBkaSJGXHACNJkrJjgJEkSdmxB0aSeoH6oZsX6hdO2L5QL3rf8kI9Yadiv8lG9SsK9ZQtLu3E0a2d5uo6NRXqgzZaWqjP+MxWhXrbr9kDo7XnDIwkScqOAUaSJGXHACNJkrJjD4wk9QKvHDWuUM/60g836PFuf7uxUD+5YvgGPR7A9Jd2L9T/NOzRQv3Duz5UqP97/FWF+vCBiwr1iqGrNnhMql3OwEiSpOwYYCRJUnYMMJIkKTv2wEhSLzB85vxCvf+DxxUPuGpooWx4u/3PMhoyq/h4q575+/oPbrUXC9UtbFKoxzGrUJ/1g5MK9eHH/6gTxiCVOANT4yJi/4iY19PjkCRpXRhgeqmIeCYilkXE4ohYEBFXRMTGnX2dlNJfU0rbd3zkhomIfhGxsLXnUH6uL0fEoIptJ0fErZ1w3XMj4ucb+jiSpN7FANO7HZVS2hjYHdgT+Hr1AVGSw3/HA4DZKaXFbeyvB87oxvFIkjJmD0wGUkovRMRNwM4A5ZmJ24EDKYWb90bEK8B/ARMofSTJ5cA3Kf03XgDsl1KaUz5/GPB3YBSwI/DzlNJW5X0jgB9SChyLgQtTShdHxADgdWDrlNLCiPga8C1gs5TSWxFxHjA4pXRmG09jAvC7dp7m94EvRcTUlNIb1Tsj4j3lce0BvAJ8I6V0TUT0A+4BLksp/TAi6oG/ALeUt3+1dHp8BHgypbRrO2OQesyqp54p1EMmVB/xxLo93gaNRur9cvjLveZFxNaUAsADFZtPBD4DDAaeBa6g9DtrDPA+4BDg5JTScuCXwPEV5x4LzEwpvVx1nTrgt8CDwEjgIODMiDg0pfQ2cC8wvnz4+PJ1962oZ7bzNCYAN7azfxZwK3B29Y7y0tIM4CpgOPBxYGpE7JhSWgF8EpgcETsAX6E0m3N+SulmYApwdUppY8OLJPUdBpje7fqIeAO4jVI4mFKx74qU0sMppVXAZpQCwpkppSXlYHIhpRd6KL3wf7zi3E+Ut1XbCxiWUpqcUlqRUnoKuKTi3JnA+IhoAHYBLi7XA8rn/qW1JxER2wENKaWOmoXPAU4vzxBVOhJ4JqV0eUppVUrpAWA6MBGgPLP0beB6SgHoxJSqPgZXktSnuITUu30kpfSHNvZVfu78KKARmB8RLdvqKo75MzAwIvamtJy0G/CrVh5zFDCiHJpa1AN/LX89k9Iy1e7AQ5RmRS4D9gGeSCm92sZYJwA3tbFvtZTSnIi4gdIsyiNV49q7alwNwM8q6mnA+cD0lNLjHV1LkpQ3A0y+Km8C8RywHBhanpEpHphSU0RcQ2kZaQFwQ0ppUfVx5cd5OqU0to1r3gFsDxxNaQlqbkRsQymgdLR8dHFHT6jsm8D9wAVV45qZUjq4nfOmAjcAh0bEfiml28rb279ZhqQuc9j4B9rdP+CFxnb3S+1xCakPSCnNB34PXBARm0REXURsFxHjKw67CjgOOIHWl4+g1PS6KCK+HBEbRUR9ROwcEXuVr7MUuA/4PO8EljuAU2kjwETEQOD9lGaB1ua5PAFcDUyq2HwDMC4iToyIxvI/e5V7XoiIEyk1936qfN60irdrLwC2zeSdWpKkteQv9b7jJKAfMJfSu4WuA7Zs2ZlSuhtYAoygjeWcct/IkZSWmJ4GFgKXAkMqDptJabnqnop6MG30vwAfAu4sNwGvrcnA6nvClGeLDqHUi/Mi8BLwXaB/eQboIuCklNLilNJVlBqCLyyffm35369GxP3rMAZJUi8WKTnDrq4TEVOBOSmlqT09FuXj4LqJ/mLqA8be279QXzjijkK9y09OL9TbTC7ulwBmNF8brW23B0ZdbTalt2ZLqjEfflf7k57b/qZ4y6fmrhyM+hwDjLpUSumnPT0GSVLfYw+MJEnKTrszMK5DS+osba1jS9L6cAlJktQpGrYaWag3r7+n6oj67huM+jyXkCRJUnYMMJIkKTsGGEmSlB0DjCRJyo5NvJKkTvHYpG0K9S79ik27pz1/QPGER5/q6iGpD3MGRpIkZccAI0mSsmOAkSRJ2bEHRpK0XqpvXHfRMZe3e/wDl+xSqDd/+85OH5NqhzMwkiQpOwYYSZKUHQOMJEnKjj0wkqT1koZsXKgP2WhJoV7YtKxQD3lqRZePSbXDGRhJkpQdA4wkScqOAUaSJGXHHhhJ0np55pjN290/ce5JhXrQn+7ryuGoxjgDI0mSsmOAkSRJ2THASJKk7NgDI0laL9P/7QdVW/oXq++9q/sGo5pjgJF6uWhooOkD7wUg1QdPn5xW7ztwzONss9FrfH3oHOqjjpuX9ueHBx3Cqmef66nhSlK3MMBIvUz90M154YTtWfS+5UzYaQ4b1a9gyhaXtntOM9Ccmjhoo6Wc8Zmt2PZrBhhJfZsBRuplXjlqHLO+9MPCttvfbgTgyRXDC9unv7Q7/zTsUX5414f47/FXcfjARawYuqrbxipJPcUAo16hZZmkeokEam+ZZPjM+ez/4HFw1VAa3i59L4bMmg/Aqmf+XnX0i9zCJoxjFmf94CQOP/5H3Txa1ZL5Z32wUI9pLN7XZUHVZx8NePLlQm20VmcywKhHuEzStlVPPcOQCQBPvLOtx0YjSb1TnwowDaO25vHPbcWqEcuZd9AlhX2NUc/K1MTu95zI4tcGstnwt7hr918AcPxTh/LwjHGM+u59pOXLe2LoNaetZZLqJRJwmWRtHTb+AQAGvNDYwyORpK7XpwLMixcP4qE9LwZKf61Xun7JEA4f+Dqz3j+NXywayQ2v7MLxTx3KkcP+xpWjb4LP3sRunME2k+/o0jG6VFLS1jLJmksk4DLJ2vnwu+4HYNvfvLHGz78k9TV9KsCsun0zdl5ySmFb/H0jtv3NUhpfepOp7x4CQONzr7LquecBuGbMviz41RzO3Gwub2+zotPH5FJJ61wm6VwNW41k8/p7gPqeHor6sD2OfahQV/e8HP3NLxbqzZ69s8vHpNrVpwLMiO+1PXuyCoinKr7u35/6LbfglJtmcPjA1/nxG2PZ8dz5nf4i2lNLJX11Oc1lktY9NmkbdulXz2nPHwCPPtXTw5GkLtclAaatZZIDxzwO0OPLJNG/Py+cvgez/uO/AVjQtJxpP5rA8Oc7f/mop5ZKclhOWx8uk6ypYauRXHTM5QA8cMkubP62f/VK6vs6JcDktEzy5gn78Mrhy5n7j6XwMmXhbtx52l4Mv71rXqx7aqmkejmtZSkN6LHltA3lMknr0pCNOWSjJSxsWsaQp3rffzdJ6gqdEmDWdplk+ku7A3T7O0qif3/qth7BvHM35f4DL2RAlJ72e//fJMZ8dy7xxuwuvf762NClkt64nLah1neZpK3ltJalNCDL5bQWzxyzOQAT557EoD/d18HR0tp5+bQPrrHtypHfL9R/WjayUG92ubN/6j5+GrUkScpOp8zArH2fx4sA3fKW2Ob93wfAs6c1c8iYR7lwxHU000zlUx6xx3zSVlvCG292yRg2RHf1enRnP9CG2JA+j7b6gVp6gYAs+4FalD4RuL+f/CuppnRKgOmNb4l99rTSS9VDB7T04tQxa3k9ty3Znr++OobpY3/DLTtdyz7nn8jwf+65cbamu3o9ursfaENsSJ9HW/1Alb1AkFc/UIv5Z32QMY33saBpGQOefLnH/38nSd2lR99G3ZVviR19Qam34T1LPscmc/ox4g+vUbdkGWnRYprfeIX9TprEbeddzHW7Xcpp7Nfp198QXfWW2OjfHyC7fiDYsD6PtvqBKnuBVteZ9AO12OPYh1jQtIyjv/nFtbrnRkf9QC29QECW/UDqPIM+/NIa24bUDSjUX511dKHejt75+0N9U48GmK5cJkmz5gAw7uRS3Uxx+aDpI69RRx1Pruxd0+5d8ZbY5v3ft3opDchuOQ26Z5kkl+W0Fi+f9kGuHPl9/rRs5Fo3T3a0nNaylAZkuZwmqXb0WIDpybfE1g0YwLjNX6GZZk793b8ylru7fQxt6Yq3xD57WnPFUhrktJwG3bNMktNyWotBH36JIXUD+Oqso9f6L9+1WU5rfO7V0rGZLadJqi09FmB68s6hi47Yleu3/b8AbPpI73ojVle8JXb0BU2rl9KArJbTYN2XSdZG5VvrgayW06A0/ht3vpLlKRj3rcU0reV5a7Oc1hIQc1tOk1RbeiTAdOYySd2AASw6YlcG3/ggzW+/3fG1R47gkgsuBBr5z4W78u4rH17rX/7doSuWStKsOauX0iCf5TRYv2WS9lQup72zlAY5LacBvHD6HgyMO3j/rBMYPu/RTn3sll6pnJbTtOFa/ru3uHHnK9c4ZnmKQj3uW4sLdW/6Xaq+r0emHzprmeSlMz7IkD8OZMr3fwL1HS9F1Q8bxnF/vIfRjY1Me2sUf/7KvjS99dZ6X7+zlZZK+q9eKukO1ctpvU3lMklnaFlOu2DEbQDMWl7PrOX1XPTajnz08Q8DcMtO1/LS+Z1yuS6z+SMr+duKJjb98eBOfdw3T9iHxy/bkccv23F1eJmycDdOPv7fGT7V8CKp9+iRGZjOWiZ5a5cVTNv2FnaeeQqjl7Q/3b/0mL356Hm3cNzg+QBc9PCH2Pqme9f72l2hK5ZKOtKbl9PWd5mkPZXLaS1LaUA2y2kt+t94L1+98f30Y8N/htu6UzXksZwmqTb1SIDp7GWSkT9f823YdQNLbwWdf/Ju9Dv0FX753gsYVt+fBU3L+cfrzmbcNx7qVR8G2BlLJX1tOa0rlkkql9Oql9Kgdy+ndbbWl9OKvxJyWE6TVJu6NcDMP6v02Rqd/Y6SiT+4mV8tPZi6laWXo8dPrufsvX8PwMlDZgJQx0ZMXzyUaccdzpjZd/Wq8ALr946SSi+d8UF2OPZR/s+WP+E7N+/b4fGtLaf1f6t3zUh11TJJW3rzu9O6QvHdacV3pgG9/t1p6lwvnL5HoR4Yay4Zvn/WCYW6s/uvpHXRrQFmj2MfAui0ZZLRVyUmvfcALh75F06+8idtHnf1oi2Zcs1Etvvp32l+fu4GXbOztTTObehSSV9bToPOXSZZG715Oa0rtLac1rKUBmSznCapNvX939KSJKnP6bYZmJYeD6DT3hLb8Mf7+PtBgzngI5N49fBlPDz+Ei59czQ/uONQ6l8v9cWMnfY6da+/xagX7uyV969ombbtrF6PtvqBWnqBgF7fD9QZ+lo/UFewH0hSzrotwLT0eADr3efRmuZFi9j0Z3ey6c/gw+wFwDhmvbOfNX8x9yabP7ISoNN6PdrqB2rpBYLe3w+0odalH6h+2DCAXt8P1J3qBpT+f1pL/UB653dRi7+tWDPCd1c/mrQ2uiXAVL4dFui0t8T2Bf1vLL1Qbmivx9r0A129aEuAXtsP1FnWth+opRcI6PX9QN1p0RG7AtRUP5Ck/HRLgKl8OyzYud4VOlpOa1lKA3rtclpn62g5rWUpDeizy2ktsylru5z2zlIa1NJymqT8dEuA6e63w9aq9pbTevtSWlfoaDmtZSkN6JPLaS1LacBaL6e1LKUBLqdJ6tUipdTmzoPrJra9U+qlVh20B2O+M5eLR/6lzWMq31oPsOr5F7preN3mscv25NHDfgxQWk77RMfLaZ/b9PHV295356fZ+mNzOm08M5qvjY6PKvF3j6QWbf3u6LFPo5a6ytoup9XKUhqs/XJay1Ia0OeW0yT1LQYY9UkupxWt7XJay1Ia1N73SFJeDDBSH9XyzjSgw3en9fV3pknqewwwUh/VspQGuJymPqFh1NaF+vHPbVWoV41YXqjnHXTJOj1+Y9QX6pVpzfff7X7PiYV68WsDC/Vmw98q1Hft/ot2r3n8U4cW6odnjCvUo757X6FOy4vPsZYZYKQ+rHnRIgCX0yT1Od6hSpIkZccAI0mSsuMSkiQpCy9ePKhQP7Tnxe0ev67Lo9cvGVKoDx/4+hrHzHr/tEL9i0UjC/UNr+xSqKt7XI4c9rdCfeXom4oX+Gyx3o0zCvU2k+9YY0y1yhkYSZKUHQOMJEnKjgFGkiRlxx4YSVIWVt2+WaHeeckp63R+/H2jQr3tb5YW6saX3izUU99d7IlpTeNzrxbqVc893+7x14wpfqjqgl8VP2/szM2KN5N8e5sVHY6hVjkDI0mSsmOAkSRJ2Wl3Camtj7CWJEnqSc7ASJKk7NjEK0nKwojvde1N3Ko/0DSeWo9z+vcv1PVbblGoT7lpRqGuvlnej98YW6h3PHd+u9erZc7ASJKk7BhgJElSdgwwkiQpO/bASJK0nqp7Xl44fY9CPes//rvd8xc0LS/U0340oVAPf94Pb2yLMzCSJCk7BhhJkpQdA4wkScqOPTCSJK2lN0/Yp1C/cnixh2XuP7bf8zJl4W6F+s7T9irUw2+352VtOQMjSZKyY4CRJEnZMcBIkqTs2AMjSapJ1fdwqdt6RKGed+6ma5xz/4EXFuoB0f7L6Hv/36RCPea7c4tjeGN2h+NU65yBkSRJ2THASJKk7BhgJElSduyBkST1Sc37v69QP3tac6E+ZMyjhfrCEdcVz6d4fMm6vWyO2GN+oU5bbVk84I031+nx9A5nYCRJUnYMMJIkKTsGGEmSlB17YCRJfVJ1z8tDB1zawRnFv+lnLa9f44jblmxfqP/66phCPX3sbwr1LTtdW6j3Of/EQj38nzsYktrkDIwkScqOAUaSJGXHACNJkrITKaWeHoMkFRxcN9FfTNpgsefOhXreqQMK9SZz+hXqEX94rVDXLVm2xmOmRYsLdXPVfVxePWmvQn3beRcX6udXLS/Up43ab41rqGhG87XR2nZnYCRJUnYMMJIkKTsGGEmSlB3vAyNJ6pPSrDmFetzJ7R9f/clHrX0SUkeaPlLVR1M1T/Dkynetx6OqNc7ASJKk7BhgJElSdgwwkiQpO/bASJK0nuoGFO8tM27zVwp1c1Unzam/+9dCPZa7u2ZgNcAZGEmSlB0DjCRJyo4BRpIkZcceGEmS1tOiI3Yt1Ndv+3/bPX7TR5w36Cx+JyVJUnYMMJIkKTsGGEmSlB17YCRJPaL6HirV/SSDb3ywUDe//XaXj6kjDSNHFOpLLriw6ojGQvWfC4vP6d1XPlyomzptZLXHGRhJkpQdA4wkScqOAUaSJGXHHhhJUrd46YwPFuodjn20UP+fLX9SqL9z875dPqb21A8btsa24/54T6Ee3VjseZn21qhC/eevFJ9D/7fu7aTRyRkYSZKUHQOMJEnKjgFGkiRlxx4YSVK3eGuXFYV62ra3FOqdZ55SqEcvmd3lY6q09Ji9C/VHz7tljWOOGzy/3ce46OEPFeqtb7Lnpas4AyNJkrJjgJEkSdkxwEiSpOzYAyNJ6hVG/ryx44PWQd3AgYV6/sm7Fep+h75SqH/53gsK9bD6/ms85oKm5YX6H687u1CP+8ZDhbp57Yaq9eAMjCRJyo4BRpIkZccAI0mSsmMPjCSpV5j4g5sL9a+WHlyo61a231Hy+Mn1hfrsvX9fqE8eMrPd8+vYqFBPXzx0jWOmHXd4oR4z+65Cbc9L93EGRpIkZccAI0mSsmOAkSRJ2YmUUk+PQZIKDq6b6C+mPmjVQXsU6jHfmVuoLx75l+4cDlcv2rJQT7lmYqHe7qd/X+OcVc+/0KVj0ppmNF8brW13BkaSJGXHACNJkrJjgJEkSdkxwEiSpOzYxCup17GJtzbUDR5cqF/7yM6F+tXDlxXqh8dfUqgvfXN0of7BHYcW6vrXix8OOXba68Xrv/5WoV71wosdjFg9wSZeSZLUZxhgJElSdgwwkiQpO/bASOp17IGR1MIeGEmS1GcYYCRJUnYMMJIkKTsGGEmSlB0DjCRJyo4BRpIkZccAI0mSsmOAkSRJ2THASJKk7BhgJElSdgwwkiQpOwYYSZKUHQOMJEnKjgFGkiRlxwAjSZKyY4CRJEnZMcBIkqTsGGAkSVJ2DDCSJCk7BhhJkpQdA4wkScqOAUaSJGXHACNJkrITKaWeHoMkSdI6cQZGkiRlxwAjSZKyY4CRJEnZMcBIkqTsGGAkSVJ2DDCSJCk7BhhJkpQdA4wkScqOAUaSJGXHACNJkrJjgJEkSdkxwEiSOl1E7B8R83p6HOq7DDCSVEMi4pmIWBYRiyNiQURcEREbd/Z1Ukp/TSlt39mPWy0i+kXEwtaeQ/m5vhwRgyq2nRwRt3bCdc+NiJ9v6ONo/RlgJKn2HJVS2hjYHdgT+Hr1AVGSw2vEAcDslNLiNvbXA2d043jUTXL44ZQkdYGU0gvATcDOABFxa0ScHxG3A0uB0RExJCIui4j5EfFCRHw7Iuojon9EvBERO7c8XkQMK8/uDI+IAyPi+Yp9IyJiekS8EhFPR8Sk8vYB5XOGluuvRcSqiNikXJ8XERe18zQmAL9rZ//3gbMjYtPWdkbEeyJiRkS8FhHzIuLY8vZ+ETE7Ik4v1/URcXtEnBMRhwFfBY4rz2Q92P53Wl3BACNJNSoitqYUAB6o2Hwi8BlgMPAscAWwChgDvA84BDg5pbQc+CVwfMW5xwIzU0ovV12nDvgt8CAwEjgIODMiDk0pvQ3cC4wvHz6+fN19K+qZ7TyNCcCN7eyfBdwKnF29o7y0NAO4ChgOfByYGhE7ppRWAJ8EJkfEDsBXKM3mnJ9SuhmYAlydUto4pbRrO9dXFzHASFLtuT4i3gBuoxQOplTsuyKl9HBKaRWwGaWAcGZKaUk5mFxI6YUeSi/8H6849xPlbdX2AoallCanlFaklJ4CLqk4dyYwPiIagF2Ai8v1gPK5f2ntSUTEdkBDSqmjZuFzgNMjYljV9iOBZ1JKl6eUVqWUHgCmAxMBUkpzgG8D11MKQCemlJo6uJa6SUNPD0CS1O0+klL6Qxv7nqv4ehTQCMyPiJZtdRXH/BkYGBF7AwuA3YBftfKYo4AR5dDUoh74a/nrmcB/UerJeYjSrMhlwD7AEymlV9sY6wRKS2DtSinNiYgbKM2iPFI1rr2rxtUA/KyingacD0xPKT3e0bXUfQwwkqRKqeLr54DlwNDyjEzxwJSaIuIaSstIC4AbUkqLWnnM54CnU0pj27jmHcD2wNGUlqDmRsQ2lAJKR8tHF3f0hMq+CdwPXFA1rpkppYPbOW8qcANwaETsl1K6rbw9tXOOuoFLSJKkVqWU5gO/By6IiE0ioi4itouI8RWHXQUcB5xA68tHAPcAiyLiyxGxUbkhdueI2Kt8naXAfcDneSew3AGcShsBJiIGAu+nNAu0Ns/lCeBqYFLF5huAcRFxYkQ0lv/Zq9zzQkScCOwBfKp83rSKt2svALbN5J1afZLfeElSe04C+gFzgdeB64AtW3amlO4GlgAjaGM5p9w3ciSlJaangYXApcCQisNmUlquuqeiHkwb/S/Ah4A7y03Aa2sysPqeMOXZokMo9eK8CLwEfBfoX54Bugg4KaW0OKV0FaWG4AvLp19b/verEXH/OoxBnSRSchZMkpSXiJgKzEkpTe3psahn2AMjScrRbEpvzVaNcgZGkiRlxx4YSZKUHQOMJEnKjj0wknqdg+smurYtCYAZzddGa9udgZEkSdkxwEiSpOwYYCRJUnYMMJIkKTsGGEmSlB0DjCRJyo4BRpIkZccAI0mSsmOAkSRJ2THASJKk7BhgJElSdgwwkv4/e/ce51R17n/8+2SGGRhugoCIFhEQsFBBBO9KrUewiPYmaFuwWq1iKYq3U6se29JqjyDeEForKv7ssVVEsVCrolUUQRRFFCsgohYEQVCUO8xk/f5IMmRlMpkZyExmZT7v18uXPDs72TtRsr9Z69l7A0BwCDAAACA4BBgAABAcAgwAAAgOAQYAAASHAAMAAIJDgAEAAMEhwAAAgOAQYAAAQHAIMAAAIDgEGAAAEBwCDAAACA4BBgAABIcAAwAAgkOAAQAAwSHAAACA4BBgAABAcAgwAAAgOAQYAAAQHAIMAAAIDgEGAAAEhwADAACCQ4ABAADBIcAAAIDgEGAAAEBwCDAAACA4BBgAABAcAgwAAAgOAQYAAASHAAMAAIJDgAEAAMEhwAAAgOAQYAAAQHAIMAAAIDgEGAAAEBwCDAAACA4BBgAABIcAAwAAgkOAAQAAwSHAAACA4BBgAABAcAgwAAAgOAQYAAAQHAIMAAAIDgEGAAAEhwADAACCQ4ABAADBIcAAAIDgEGAAAEBwCDAAACA4BBgAABAcAgwAAAgOAQYAAASHAAMAAIJDgAEAAMEhwAAARrgG3AAAIABJREFUgOAQYAAAQHAIMAAAIDgEGAAAEBwCDAAACA4BBgAABIcAAwAAgkOAAQAAwSHAAACA4BBgAABAcAgwAAAgOAQYAAAQHAIMAAAIDgGmgTOzk8xsWa73AwCAmiDA1FNm9pGZbTezLWa2zsymmlmzbG/HOfeyc657tl83lZkVmdmGdO8h/l7Xm1nTpGUXmdmLWdjub8zsL/v6OgCA+oUAU7+d6ZxrJqmvpH6SbkhdwWJC+O94sqS3nHNbKnm8QNLldbg/AICAhXDga/Ccc59I+qekXpJkZi+a2U1m9oqkbZI6m1lLM7vPzNaa2Sdm9nszKzCzYjPbZGa9Eq9nZm3jozvtzOybZrY66bEOZjbdzD4zsw/N7LL48sbx57SJ19ebWamZtYjXvzOzOzK8jcGSnsrw+HhJV5vZfukeNLMeZjbbzD43s2VmNiy+vMjM3jKz0fG6wMxeMbMbzex0SddJOic+krU48ycNAAgFASYAZvY1xQLAoqTFIyRdLKm5pI8lTZVUKqmrpCMlDZR0kXNup6THJf0w6bnDJM1xzq1P2U5E0kxJiyUdJOlUSWPMbJBzboek1yUNiK8+IL7dE5LqORnexmBJ/8jw+EJJL0q6OvWB+NTSbEkPS2on6VxJk83s6865XZKGSxprZodLulax0ZybnHNPS7pZ0iPOuWbOud4Ztg8ACAgBpn6bYWabJM1VLBzcnPTYVOfcu865UkmtFQsIY5xzW+PB5HbFDvRS7MB/btJzfxRflqq/pLbOubHOuV3OuZWS7k167hxJA8ysUNIRku6K143jz30p3Zswsy6SCp1zVTUL3yhptJm1TVk+RNJHzrkHnHOlzrlFkqZLGipJzrklkn4vaYZiAWiEc66sim0BAAJWmOsdQEbfdc49V8ljq5L+fIikRpLWmlliWSRpnRcklZjZMZLWSeoj6Yk0r3mIpA7x0JRQIOnl+J/nSLpNsZ6cdxQbFblP0rGSVjjnNlayr4MVmwLLyDm3xMxmKTaK8l7Kfh2Tsl+Fkh5Kqh+UdJOk6c6596vaFgAgbASYcLmkP6+StFNSm/iIjL+ic2Vm9qhi00jrJM1yzm1O85qrJH3onDuskm3Ok9Rd0vcUm4L6t5l1VCygVDV9dFdVbyju15LelDQhZb/mOOdOy/C8yZJmSRpkZic65+bGl7sMzwEABIoppDzgnFsr6VlJE8yshZlFzKyLmQ1IWu1hSedI+rHSTx9J0muSNpvZL82sSbwhtpeZ9Y9vZ5ukNySN0p7AMk/SSFUSYMysRNLRio0CVee9rJD0iKTLkhbPktTNzEaYWaP4P/3jPS8ysxGSjpJ0fvx5Dyadrr1OUqdAztQCAFQTX+r54zxJRZL+LekLSY9JOjDxoHNugaStkjqokumceN/IEMWmmD6UtEHSFEktk1abo9h01WtJdXNV0v8i6VuS5sebgKtrrKTya8LER4sGKtaLs0bSp5JukVQcHwG6Q9J5zrktzrmHFWsIvj3+9Gnxf280szdrsA8AgHrMnGOEHbXHzCZLWuKcm5zrfUE4TosM5YsJgCRpdnSapVtODwxq21uKnZoNAEDWEGBQq5xzf871PgAA8g89MAAAIDgEGAAAEJyMU0g00gHIlsoa8QBgbzACAwAAgkOAAQAAwSHAAACA4BBgAABAcAgwAAAgOAQYAAAQHAIMAAAIDgEGAAAEhwADAACCQ4ABAADBIcAAAIDgEGAAAEBwCDAAACA4Ge9GDQBAdVlxsVfvGvANr77tnkle3Sdl/c7TL/Hqw0YvyOLeId8wAgMAAIJDgAEAAMEhwAAAgODQAwMA2CuRxo29eulEv+dl+eA/pTzDP+TsdmVePfusCV7989En7tsOIq/lVYCJNG6sWR/MkyQVWGxwqcxFddsXh+nFbx+u0lWrc7l7AAAgS/JqCmnNyL6Kyikqp92uTLtdmaJyGtNquVbc2loyy/UuAgCALMirAAMAABqGvJpCmnnlOP19awfN29y1fNnP27ykjoVNtOTEBzRw4CUqemZhDvcQAMKVep2XqntegNqTVyMww0dfqfvOHKglR0W1+Oo+Wnx1Hz25+Yjyx2fed7e+OP+4HO4hAADIhrwagWny5GvadUpfrRl2vKZfeKskqWujPb8YNkVL1fa5/6g0VzuYI8nNzVKswZnmZgBAyPJqBGbX6f11z9S7tHjkRHVtVOyFF0k66+ZrVLr6kxztXe4kNzcnGpxpbgYAhCy/RmBeXa6OhU0kSVetPVaSNOHAV7UlulN9n7pc3ae8LpfLHcwReoMA1FikoMKiZZNTel5Or1nPy8hVA7y6b4uPvfonLd736v1faeXV/7mtm1c3nc69khqyvAow0S1bdeayszSz+9814cBXy5efff5odXu+YYYXKdYb1Gzp5ypbtkKSVHZKXz05cZNGt4p9Wcy8725964Yr1Grq/FzuZp1jag0AwpVXU0iutFT2na/U86FfeMtHTJqpyBE9crRXudfkyde0q0MLrfqf47Xqf47XxAfuLg8v0p7eoIaGqTUACFdejcBIUnTzZnUdv0x9vhwtSXpi5Hj9uPlaNZo2Ww/16iK3e1eO97Du7Tq9v6bcc3v59JpUsTeo7eqGNfoi7Zlak1Q+vcbUGgCEwZyrfGLltMjQ4Gdd3Al9tOKcYi37wWRd+J9T9NnPOii6ZGmud6tOFezXUk+++6/y+qq1x/q9QaMWyZU2tHOzpO3fOVrNln4uSSpbtkJlp/TV4Ikvlo9O7XS7G+TUWm2ZHZ1W7SGtfPjuCd3y+/tVXDbonhq9Ro9/XeTV3UYu9+pIa7/H5eH507y6xIr815s2yqu7jnlVyH+VfXfk3QhMKnvlLfVY0U79V4zWgl/eqb5nXa6Dl+R6r+pWcm+QpPL+oIbeG5Q47V5S+an3nHa/5wZ9yfcVozcIQH2TVz0wlSlbt14H3DVPO1ypxv30flm/XrnepTqV3BuU3B/U0HuDEqfdJ596nyyE0+6tuFi7B/bTLR8u0DNr3tL7E4/Z59dcM7JvhfuK0RsEoL5pEAHGiou15enOKrEiDWyyVf2mLM71LtW5RG9Q1/HL1Ofu0fqwdId+3Hythk+bLWtUVPUL5KHEafepp95vie5Ut1kj1W7K67ncvSpFGjfWsruP0DMP3KOeRYXa7co0+6wJ+/y6M68cF+8PaqVrP+2vaz/tr/+UbpckLTnxAe0aeNQ+bwMA9lXeTyFJ0tpHOmvhN/5SXjeyMkkVr3GQ78o2xvo9Dv7DPP3ipVHlvUHPvNRTn/2sc73sDbLiYu0a8A3dds8k9SkuVufpl+iw0dm59kNiak2Sd+p9CFNrVlyspRO/USv3nhk++kpJKj/1ntPuG4ZPxxzv1UsH3ZlmLf8377u7/AnW0Vde5tXdnnnHq6Pbtvn11q1e/et1J3n1+Pb+3/XCA/3nhygxRZuQfCmHdArM/8zLXNSrb/viMK9+8duHe3U+T/k2iBEYAACQX/I2wESO6KHIET20/E9H62997itf3mvuBXr6f0/O4Z7VD/bKW+px04fqf8to3dvxef3nrNa53qUKamuKJCHRG5R67aB63xsUKdCyyf7oy8hVA/TnLzvpgIJC7f9KK239wd73wjR58jXv2kFcNwhAfZR3ASZSUqINFx+nG2Y8rBtmPKzlZ/7Ra84sXNxMLf7KqXdS/W1utuLiWp0iSRbdvHnPtYMC6Q1aPuVILT99z+msPf51kdac6vTUoN4qk9ODnZ7T2pP2vtF21+n9K9xXLFkIzc0A8l9e9MAUHH6Y9Mk6rR/WU2eOnqPr2tyddr0j7h2tzv+3ql6dFmvFsYNDbfV5VLXtzU8epBJ7QwObbNX8KYv1ep8c9wbFRxck/z4rI1cNUN8WH+snLd7X/q+00n9u65bV+6CUbfw8iN6gT8ccH+9NiOjdXaUafeVl6vbMO4pu26bo1q369bqTNL79gn3qFWjyauxaHcnNzdxTLP98dulxXv38VeO9OiK/VyOdEZOu8OoOT/j9HH63RtXeP2N/f8Ebfrl7R8ohK/V+TdGyGm6x7q0Z2dero3ol4/pRl/k9jWnlX1tnyq0neHWnc1N+bGS49lto8iLArD2lrW4Y84yGNH1ekfig0is7GpU/vnxXe0184LvqOH6+SuvRf7xI48ZaOjF+sB78J0l7pkl+PvrEWt9+fWxuXj7lyAoXy+rxr4vUbeRyfdq6t4bPX64HOz2nHid1V9fp2d9+fb1uUOJg8/xV48sPLCMmXaEOT8zzDhLvn7G/9Eb8iz5SsFdf6NEtscbK1PuKhdDcDKDhqPUppNTrVGTrWhXJNvXbpbOaflEeXq5ae6wueO5CXfDchRo1ZaRmfP8EdRg3r14lz+QpktqeJklVX3uDYqMLfyyv391Vqm/94ufqdskyRbduVemq1eVnKdTm2Qj1bWotUlKi4aOe0fBRz6hlJBZeXtnRSEVfVfz/ObrpS/1gxRla9l/3avPQ/nu1PVdamva+YvW+NwhAg1KrIzCJEYbk0QVJWR9h6PbThRqi5GtT7FY37bmGR70bVEw0Yabcir42p0kiJSVaP7y3JOmuayfp6GKn5HsixXqDMp/OV5s+u/Q4b3RBqr0RhqrUt6m1pZMO199bvVxev7C9sSZ07ak2qngac3THDpWObC49J60dVKrmj+z9dpPvK8Y9xQDUN7UWYOqqCTNEdTVNEkpvUGKEIXl0QVKVIwwDho5S80ey35Bd36bWjuu20qvHXzBcES2q8nnHdF+pL/Zx26H0BqFqkZISrx4+6hmvTvz9yyR5al5K/3d0X0Q3fenVP1hxhlcv+697vXrAUP/eSLXxfZBtM68c59WJG8omJG4sW5mft3nJq/fcpDdmyYkPePXAgZd4dT7doLZ2AkyaEYbE6IKkWmvEDEG6JkxJWW/ElNL3Bkl7voTqS29Q8ghDYnRBUq2PMKSKHNFDS3/eQk/1uUOJ0alecy9Q6xklaqEcfTEee4T+56A/le/PyW8PU8v579R5H0p97Q0C0HDVSoBJHWFIHl2QVOuNmPVV6jRJYopEUq1MkyR6g5TUGyRJM9/oo5KPGumQGZ+pw3u5mzZKSB5hqO7ogpSdEYbEr9L1w3vXy6m13S2KvNOY161vqRY5unN4rDdovXb8d6w3aOKzZ8stJMUAyI2sB5iqTvOUlLURhpCkmyapbPg1W9Mk6XqDJJX3B9WL3qCkEYa6Gl1InVqTlHZ6LddTa3tr/QltJEkLFndVN72Wtdetb71BABq2rAaYykYYUq8FUBeNmPVNummSdFMkUu1Ok9Q3ySMMdTW6EMrU2t6IlJTo1FHzdcvGnur2izeqfkIN1LfeINTM0kn+PXKSG8PTeWF7xZ6YxPRuQmXfYXsrumOHV5eObO6v8Jxfrh3kf1+E8F2ZuNdYQrOln3t12bIVfn2Kf92YJydu8urkq2SnM/M+/8fZt27wr90T8j3NshZgcjHCEJJcTpPkm30dYUidWpNi02v1bWqtpgra7K9PRnTX39tNVLeZl6pbNDujL/WyNwhAg5d3txIAAAD5L2sjMEyRZJCDPo98lY0pkoq9QVLytYPq04Rm4Zbd+k/p9vJTJa89+mk9cuq3Vfi8/943XHychox6SU+2eVbPbm+q9nP2/bdJ4tpB9bG5GQCyFmCYIqlcLvo88lFtTZHUZzZvsYbdfI0evS52n5oLWqzSOQ/erSMfH6PiDQUq3iR1/MFKPdZpvA4ubKKdbrd++/sL1OqvNZ/XTm5ullTptYNCbW5u6FKvJ1SV8RcMr7Csut/rdeWY7v57CuFY0uRJ/3trV0qPy5phx3v19Atv9erUm6tWZVPU/5uaeif5kP8eZyfAMMKQdbV1Jkl9kzzCUNnoglQ7IwyhaHPPfJ1x0DWSpHcuvFslVqRlP5icslYTHf63Ueo0a5davbB3TXnJzc2SKtxXLOTmZgD5JysBhhGG7KrNM0nqm+QRhuTRBUlZH2EIWec7l0mSvnfyED1x2Kzy5a/saKSLFpynjlML1fWFN/fpEv/52twMID/l7G7UDWWEoaYa4jRJYoSh8tEFKRsjDCEr2xg71dINLtFJ3x+lk65coLe+OFjud23V5cU3Y4/t4zZC6g0CgJwEmIY0wiAxTVIdne9cVmF0QcruCEM+iG7bppZ/eVVv/0WKaJWkVbneJdRXxx7hlbFp/mR+L8XJbw/z6pbz36nwkkwc7rtdp/t3iZ9yz+1enXpvo9T/TjV11s3XeHXb1fnzA7BOA0xBm/0lqcGNMFQ2TZKYIpGYJinb+Lk3uiAp6yMMAID8kZUAU50RhsTogqQGOcLANEnVkkcXJEYYAACVy0qAqWqEIXl0QVKDHGGQ0k+TJM7wYJoEAIDqy9oUUtUjDLHRBUkNdoQhdZokMUUiiWkSAFm1u0WRV1d1/ZB161t6NWeT1o4mry736oo9L76r1h7r1RMO9G/fsSW606v7PnW5V3ef8rpX59MxJqs9MJWNMCSPLkhq0CMMNGECALDvshpgKhthYHQBAABkU9bPQmKEAQAA1LacXcgOAIBMEhc8rcyCxV29OoSLoka3bPXqM5ed5dUzu//dq1N7XlKdff5or+72fP72vKRqOOcxAwCAvEGAAQAAwSHAAACA4NADAwCoFyIlJV596ij/emG3bOzp1SHeS8+lXF8n8p2vvLrn9b/w6ndH3J3x9UZMmunVjwz7lldH315a010MBiMwAAAgOAQYAAAQnIxTSLOj06yudgQAAKC6GIEBAADBoYkXAJATBW329+pPRnT36r+3m+jV3WZe6tfR+n/huqpEN2/26q7jl3l1ny/9C9U9MXK8V/+4+VqvbjRttlc/1KuLV+fTvQgZgQEAAMEhwAAAgOAQYAAAQHDogQGAPFS4ZbdX/6d0u1d3LGzi1dce/bRXP3Lqtyu+5vP7duG4DRcf59VDRr3k1U+2edarn93e1Kvbz8n/39xlGz/36oP/MM+rf/HSKK9ecU6xVy/7wWSvfuYl/+J/n/2ss1dHl4R7obv8/78BAADkHQIMAAAIDgEGAAAEx5xzud4HAPCcFhnKF1OWbbjE7z959Dr/eiKpPTHbXMXrhRz5+BivLt5Q4Neb/PU7/mClV9/Z6TGvPjhlmzud37fzrRuu8OpWU/2bO0IqOKCdV685p6tXL/jlnV7dd9LlXn3wzX6PTX1U2V0BGIEBAADBIcAAAIDgEGAAAEBw6IEBUO/QA1P7Ph7r98S8c+Hddb4Ph//Nv6ZJp1l+303BC2/W5e7kpUdX+31Dc3e08uqJ557t1W7hklrfp5qiBwYAAOQNAgwAAAgOAQYAAASHeyEBQAPU+c5lXv29k4d49ROHzarxa76yo5FXX7TgPK/uONU/5HRN6XFxuyteewaZWbF/L6TNTx7k1SXm379qYJOtXj1/ymKvfr2Pf22f+owRGAAAEBwCDAAACA4BBgAABIceGABogMo2fu7VbnCJV5/0ff8aLZJ00pULvPqtLw72X+N3bb26y4uZr+PCxX723dpHOnv1wm/8pUbPb2RlKUvogQEAAKg1BBgAABAcAgwAAAgOPTAAAEW3bfPqln95tcI6b6e0V0S0KmWN1Br7KnJED69e+vMWXv1UnztSnlGsTHrNvcCrW8/we59aqOJ/9/qKERgAABAcAgwAAAgOAQYAAASHHhgAAHIkUuL3oKwf3tur77p2klcfXZx69ZzMPS+pChc38+oWf51Xo+fXJ4zAAACA4BBgAABAcAgwAAAgOPTAAACQJQWHH+Yv+GSdV64f1tOrzxw9x6uva3N3VvfniHtHe3Xn//Ov1VOa1a3VLUZgAABAcAgwAAAgOAQYAAAQHHpgAADIkrWntPXqG8Y849VDmj7v1ZEqxhFe2dEo4+PLd7X36okPfNerO46f79WlLvU6MuFiBAYAAASHAAMAAIJDgAEAAMGhBwYAgCzZ1G+XV5/V9IuUNTKPG1y19livnvlGH68u+cjviTlkxmde3eG9cO9tVFOMwAAAgOAQYAAAQHAIMAAAIDjm8uiccAD54bTIUL6YAEiSZkenWbrljMAAAIDgEGAAAEBwCDAAACA4BBgAABAcAgwAAAgOAQYAAASHAAMAAIJDgAEAAMEhwAAAgOAQYAAAQHAIMAAAIDgEGAAAEBwCDAAACA4BBgAABIcAAwAAgkOAAQAAwSHAAACA4BBgAABAcAgwAAAgOAQYAAAQHAIMAAAIDgEGAAAEhwADAACCQ4ABAADBIcAAAIDgEGAAAEBwCDAAACA4BBgAABAcAgwAAAgOAQYAAASHAAMAAIJDgAEAAMEhwAAAgOAQYAAAQHAIMAAAIDgEGAAAEBwCDAAACA4BBgAABIcAAwAAgkOAAQAAwSHAAACA4BBgAABAcAgwAAAgOAQYAAAQHAIMAAAIDgEGAAAEhwADAACCQ4ABAADBMedcrvcBAACgRhiBAQAAwSHAAACA4BBgAABAcAgwAAAgOAQYAAAQHAIMAAAIDgEGAAAEhwADAACCQ4ABAADBIcAAAIDgEGAAAEBwCDAAgKwzs5PMbFmu9wP5iwADAA2ImX1kZtvNbIuZrTOzqWbWLNvbcc697Jzrnu3XTWVmRWa2Id17iL/X9WbWNGnZRWb2Yha2+xsz+8u+vg72HgEGABqeM51zzST1ldRP0g2pK1hMCMeIkyW95ZzbUsnjBZIur8P9QR0J4X9OAEAtcM59IumfknpJkpm9aGY3mdkrkrZJ6mxmLc3sPjNba2afmNnvzazAzIrNbJOZ9Uq8npm1jY/utDOzb5rZ6qTHOpjZdDP7zMw+NLPL4ssbx5/TJl5fb2alZtYiXv/OzO7I8DYGS3oqw+PjJV1tZvule9DMepjZbDP73MyWmdmw+PIiM3vLzEbH6wIze8XMbjSz0yVdJ+mc+EjW4syfNGoDAQYAGigz+5piAWBR0uIRki6W1FzSx5KmSiqV1FXSkZIGSrrIObdT0uOSfpj03GGS5jjn1qdsJyJppqTFkg6SdKqkMWY2yDm3Q9LrkgbEVx8Q3+4JSfWcDG9jsKR/ZHh8oaQXJV2d+kB8amm2pIcltZN0rqTJZvZ159wuScMljTWzwyVdq9hozk3Ouacl3SzpEedcM+dc7wzbRy0hwABAwzPDzDZJmqtYOLg56bGpzrl3nXOlklorFhDGOOe2xoPJ7Yod6KXYgf/cpOf+KL4sVX9JbZ1zY51zu5xzKyXdm/TcOZIGmFmhpCMk3RWvG8ef+1K6N2FmXSQVOueqaha+UdJoM2ubsnyIpI+ccw8450qdc4skTZc0VJKcc0sk/V7SDMUC0AjnXFkV20IdKcz1DgAA6tx3nXPPVfLYqqQ/HyKpkaS1ZpZYFkla5wVJJWZ2jKR1kvpIeiLNax4iqUM8NCUUSHo5/uc5km5TrCfnHcVGRe6TdKykFc65jZXs62DFpsAycs4tMbNZio2ivJeyX8ek7FehpIeS6gcl3SRpunPu/aq2hbpDgAEAJHNJf14laaekNvERGX9F58rM7FHFppHWSZrlnNuc5jVXSfrQOXdYJducJ6m7pO8pNgX1bzPrqFhAqWr66K6q3lDcryW9KWlCyn7Ncc6dluF5kyXNkjTIzE50zs2NL3cZnoM6wBQSACAt59xaSc9KmmBmLcwsYmZdzGxA0moPSzpH0o+VfvpIkl6TtNnMfmlmTeINsb3MrH98O9skvSFplPYElnmSRqqSAGNmJZKOVmwUqDrvZYWkRyRdlrR4lqRuZjbCzBrF/+kf73mRmY2QdJSk8+PPezDpdO11kjoFcqZWXuKDBwBkcp6kIkn/lvSFpMckHZh40Dm3QNJWSR1UyXROvG9kiGJTTB9K2iBpiqSWSavNUWy66rWkurkq6X+R9C1J8+NNwNU1VlL5NWHio0UDFevFWSPpU0m3SCqOjwDdIek859wW59zDijUE3x5/+rT4vzea2Zs12AdkiTnHKBgAICxmNlnSEufc5FzvC3KDHhgAQIjeUuzUbDRQjMAAAIDg0AMDAACCQ4ABAADBoQcGQL1zWmQoc9sAJEmzo9Ms3XJGYAAAQHAIMAAAIDgEGAAAEBwCDAAACA4BBgAABIcAAwAAgkOAAQAAwSHAAACA4BBgAABAcAgwAAAgOAQYAAAQHAIMAAAIDgEGAAAEhwADAACCQ4ABAADBIcAAAIDgEGAAAEBwCDAAACA4BBgAABAcAgwAAAgOAQYAAASHAAMAAIJDgAEAAMEhwAAAgOAQYAAAQHAIMAAAIDgEGAAAEBwCDAAACA4BBgAABIcAAwAAgkOAAQAAwSHAAACA4BBgAABAcAgwAAAgOAQYAAAQHAIMAAAIDgEGAAAEhwADAACCQ4ABAADBIcAAAIDgEGAAAEBwCDAAACA4BBgAABAcAgwAAAgOAQYAAASHAAMAAIJDgAEAAMEhwAAAgOAQYAAAQHAIMAAAIDgEGAAAEBwCDAAACA4BBgAABIcAAwAAgkOAAQAAwSHAAACA4BBgAABAcAgwAAAgOAQYAAAQHAIMAAAIDgEGAAAEhwADAACCQ4ABAADBIcAAAIDgEGAAAEBwCDAAACA4BBgAABAcAgwAAAgOAQYAAASHAAMAAIJDgAEAAMEhwAAAgOAQYAAAQHAIMAAAIDgEGAAAEBwCDAAACA4BBgAABIcAAwAAgkOAAQAAwSHANHBmdpKZLcv1fgAAUBMEmHrKzD4ys+1mtsXM1pnZVDNrlu3tOOdeds51z/brpjKzIjPbkO49xN/rejNrmrTsIjN7MQvb/Y2Z/WVfXwcAUL8QYOq3M51zzST1ldRP0g2pK1hMCP8dT5b0lnNuSyWPF0i6vA73BwAQsBAOfA2ec+4TSf+U1EuSzOxFM7vJzF6RtE1SZzNraWb3mdlaM/vEzH5vZgVmVmxmm8ysV+L1zKxtfHSnnZl908xWJz3Wwcymm9lnZvahmV0WX944/pw28fp6Mys1sxbx+ndmdkeGtzFY0lPVWhEOAAAgAElEQVQZHh8v6Woz2y/dg2bWw8xmm9nnZrbMzIbFlxeZ2VtmNjpeF5jZK2Z2o5mdLuk6SefER7IWZ/6kAQChIMAEwMy+plgAWJS0eISkiyU1l/SxpKmSSiV1lXSkpIGSLnLO7ZT0uKQfJj13mKQ5zrn1KduJSJopabGkgySdKmmMmQ1yzu2Q9LqkAfHVB8S3e0JSPSfD2xgs6R8ZHl8o6UVJV6c+EJ9ami3pYUntJJ0rabKZfd05t0vScEljzexwSdcqNppzk3PuaUk3S3rEOdfMOdc7w/YBAAEhwNRvM8xsk6S5ioWDm5Mem+qce9c5VyqptWIBYYxzbms8mNyu2IFeih34z0167o/iy1L1l9TWOTfWObfLObdS0r1Jz50jaYCZFUo6QtJd8bpx/LkvpXsTZtZFUqFzrqpm4RsljTaztinLh0j6yDn3gHOu1Dm3SNJ0SUMlyTm3RNLvJc1QLACNcM6VVbEtAEDACnO9A8jou8655yp5bFXSnw+R1EjSWjNLLIskrfOCpBIzO0bSOkl9JD2R5jUPkdQhHpoSCiS9HP/zHEm3KdaT845ioyL3STpW0grn3MZK9nWwYlNgGTnnlpjZLMVGUd5L2a9jUvarUNJDSfWDkm6SNN05935V2wIAhI0AEy6X9OdVknZKahMfkfFXdK7MzB5VbBppnaRZzrnNaV5zlaQPnXOHVbLNeZK6S/qeYlNQ/zazjooFlKqmj+6q6g3F/VrSm5ImpOzXHOfcaRmeN1nSLEmDzOxE59zc+HKX4TkAgEAxhZQHnHNrJT0raYKZtTCziJl1MbMBSas9LOkcST9W+ukjSXpN0mYz+6WZNYk3xPYys/7x7WyT9IakUdoTWOZJGqlKAoyZlUg6WrFRoOq8lxWSHpF0WdLiWZK6mdkIM2sU/6d/vOdFZjZC0lGSzo8/78Gk07XXSeoUyJlaAIBq4ks9f5wnqUjSvyV9IekxSQcmHnTOLZC0VVIHVTKdE+8bGaLYFNOHkjZImiKpZdJqcxSbrnotqW6uSvpfJH1L0vx4E3B1jZVUfk2Y+GjRQMV6cdZI+lTSLZKK4yNAd0g6zzm3xTn3sGINwbfHnz4t/u+NZvZmDfYBAFCPmXOMsKP2mNlkSUucc5NzvS8Ix2mRoXwxAZAkzY5Os3TL6YFBbXtLsVOzAQDIGgIMapVz7s+53gcAQP6hBwYAAASHAAMAAIKTcQqJRjoA2VJZIx4A7A1GYAAAQHAIMAAAIDgEGAAAEBwCDAAACA4BBgAABIcAAwAAgkOAAQAAwSHAAACA4BBgAABAcLiZIwAgL30w/jivfvmc8V792OaeXv2P8070avfGu7WzY8gKRmAAAEBwCDAAACA4BBgAABAcemAAAHlh+f39vHrFoEkpa5R4Vd8mH3r12U/4PS+Db/nvCttoN2ne3u8gsooRGAAAEBwCDAAACA4BBgAABIceGABAkD6YcKxXrxg0OeP6X586yqu7Tlzp1V8d18mrNw0qrfAaBx5+mFeXvfd+VbuJWsIIDAAACA4BBgAABIcAAwAAgkMPDAAgCIVfO9irZ599a8oa/nVeei8Y4dWHjn3Tq0t37vSf/cQ6r+72RMV9WPnb4716zFD/ujAPX3eGVzeZ8VrFF0FWMAIDAACCQ4ABAADBIcAAAIDg0AMDAAjCZ39q4tUdC/2el3u//Jr/+K/8HpeylJ6XqhS0alVh2aM/uc2rezYq8uo7+/iH1Y4zarRJ1AAjMAAAIDgEGKAB+GD8cZr6n7ka8u4XsqN65np3AGCfEWAQtNQDMwdn3/L7+2n5/f207EeT1K6gRH2bfKgHnrhH60cdX/WTAaAeowemnvtg/HF6+ZzxemxzT/3jvBPl3ng317tUbyy/v59WDJokKXZgPvuJ2Gcz+Jb/VrtJ8zI/uQH4YMKx3r1hvj51lLpOXKmvjuukTYNKdeDhh3EfF9RrkebNvfonh76acf17Jn/Hq9st27fvgU9+cniFZT0bPZ/xOS1Wun3aJqovpyMwDGtnxi/nysUOzn+WFDsw33z8tzXs8qs07PKrtOkbpSpIueFaQ1P4tYO9i3z1XjBCh459U6WfrlPJEwvUbeRrWnluW33v359p+3ePzuGeAsDeydkITLpfz20KmqiRFWi3Kytfr/tzP1OPcVslSWXvLsvR3ta+gv1ba9kN3bR02KTyzyCi2FUjD3vuIvUYt1Wbu+3HL2clH5xL9hyYd+4sv4pmtydiV8scM3SeHr7ujAZ5JczP/tREHQtLys/K6Pirnd4ZGAWtWunRn9ymno2KdGefQs6UABCcnASY5KHtr08dpaeGj1frgmJFFdVuJ0UV1R83HaZ/ftpL7/3XPXqgfydJ0pPnnqTo20tzscu15tMxsRGVx68Yp4MLixWVyj+DiAo0eOlZ5Z/Bk+eepG4jl5YfnCXl1QE6OcRJqjTMfjYuWn5wTj0wSxycI82blw+1J4bUU4fSP/nJ4eVD4Qx5AwhRnQaYxH0skn89Pz9ivNoWFGtzdJdOenWkzJwu+/oLevz6gWr6/Hu6eW4fXdfmLUnS7r8W6qkfHa/o4vfqcrdrzdqrjtdTl4+TJO8zaF6yQ/P7PKJ7v/yaCn7eWDc/GvsMEu8/cXCWlDcH6E/HHO+FOEmVhtmy+MznPZO/k3aOO+SDc7qRuGSJEJdpNDKyfyuNbDlHT28v0QF/ioXb1E9h68GxT/nVndL+/1yhMiHbCvZv7dXLbujm1YmgntDICrw69b99Vbo/9zOvToxcJ4Q4gh3Z378Oy8iWc7z66e3+dWAS/78n7Ovf/sTfk0xeTbm0zP7/XOHV/N2qPZyFBAAAglOnIzCJqygmhv9nHnWP2hYUa+6Oxhp/9vk69KPV+t9F/ywf+m8yY7Nm/ulkXXdDbATm4v1W6L4BZ+iAxXW517Vj57f768nLxqltQbEklX8GnVZ8pCELPpK0Z4Qh8RlcvN8KTTnlDO/Kj6GNMKSTGIlKHoWSJDOnyMIW6vjACkW2fVk+GleogvLRhXTvPtTRhdRRqNQRKEnedGJl06krfxIb6fz1Hy7Q/qXz067Tq/+HkqQ715ymss8+y/6bAYBaVmcBJnleXoodnC/41Z364QeDtfn6gxVZtEifjDm+wtB/+2nLdezpsVuiv9rvIZ183utadldd7XXtKW0aUYfCYv3wg8GSVP4ZFHTq6A3/O+35DF7t95AGDH9d0p5hy5AO0OkkB7lEiOu46B1vncT7Sw6z19/1Ux1Qmv4UyRAPzulCXGqAk1Qe4jJNp54yJNb8bZWMfltxsbo0i30ur7/dRd20sXbeFADUoroLMPF5eSk+b3na55Kk1fd3VauXY78S0/5ytojM9vzO/lW7F/Sd869Rq6npf1mGYuuI2AFp9f1dJan8M0j76znpM/hVuxcklejONadJUjAH6MokB7lEiKtM+2nLdcVPT9LEDvPSBlkrjo1mhXZwrirEJQfU5NG4ykYjmxfuyLi9LUP6aFz7P0qSOj8WcvytXxIN+QmPXzHOqw8uLPbq1Hy5O2U4MZqyxh83+ZcGSIzKJbz3X/d4deLkh4Qnzz3Jf/0ATohIfB9W5td/uMCrKxtx3FuJH0OZJL6LE0L/Tg5JnfXAJP+P+Os/XKB2zbZUWCfdL2drXKytb7fW1rdjDXGtC4q1tYPVwR7XrnTvX0r/6znxGUix9y/FDs6vv92ldneyDiQHucjLlYcXSZJFyg/Ov2r3gr44/zjv4S1D+sQPzgslhXNwTg5x/3v+eXKLKr9YYftpy3XswtiI5MnnvV7jbRUc1lln/uZf5XXR68trvsMAUA/UWYBJHJil2MF56x8P8h5PHdZOKF21Wge9tFsHvbS7bna0jqS+/4R0v54Tn4EkFSp2pkLnx8qCOUBnUlmQS8ca7/kFmxpkEwfmEA/ONQ1xyaNxqSFOkjbtjvWa7W5uksX+seJibbzwOF381LO6cL+3NHFTZ0nSxu/3qvB8AAhBnQWY1AOzlUmrS3dq46mx5dX95bzblSmSB1km+f0nPoOaKHp9eTAH6EwqC3LplK5aXX5wTkg9MId4cK5piKtqNHLR5D6KyumNX96tpnPaqOmcNuo5b7cWjJ2kb5d8oRMfuFoP3BvrvdrRJvzRTAANU04uZLf5UGlja+m0GVer2//brEiaYe3Kzr7/1/bW6jA+7PvcfDz2OO1qHS1//9Ke6xWk/nq2oiJtGN5XN1z7kCTpH9ua6YySLeUH51YPht0LlBzkWk2tev1Fk/soetNcFapA3U9/X4f+YLfGtZ+kUpWp931XS5KKPzeNvubuYA7OW/94kHRn9daNjca1ly6ofJ1WU+erR+9R+vNZ92pal2fKl39Qul3Dbr1Gh0ycV6FfAzW39ir/M0xc0ykhcYZhwuboLq9OnG2XkNzrJ0mRhS28uuMD/vVFEo3dCTfP7ePVietnJez+q/91/9SP/P2vj9fXSh65T6eyRvW9leijS0jMCmSSOpUfQt9dvqizAJP8y3nq8Lt1VLF0zafHaO7b/XXDtU/qxMbrNHFTT43eb6U2fr9X2gNzRBHdOPF8HaBwA4w7oY/3/t+7vNR7PHGAfuOXd2vo2YN0aNON5QfoiCL6n0nn64yADs6ZpAa56pwQ3mzNbkUV1Us7GpcfnJMPzFLFZsr6rqYhLiHTaGTXK17Vrb/qr98M7i1J2tk8ov2nLdYB2/y/O53P+kDbJ+zljgNADtVZgEkcmCWpf7GpVGUqUFQLxu759Zzul/OGi4/T531jU0qpXfkh+mCk6cji2DuJuoohJN2v5w9Kt2vQP6/Q0jMnVVg/VFUFuUwiiuiqOy9R89VllR6YpTAOznsT4hKqGo2M7tihkscXSJJK5J/1ctBDS6VrpKKCUm3fqz0HgNyqswCzo7Vpm4sNoTa2QhWqQOPaL6xyWPublyzQze1jX8IPfnWIOkxdEux1T9ZeebzePGWCpEK9sL2Z3rm+t4q0sMJ6yb+edzaP6KTLFmjpmZP04FeHlK/T+awPJKneH6ArU1WQSycRZhNBtuTxBRUOzFI4B+d9DXH7MhoZ3bK16pUAoB6rswBT2iQWXBK6Pn2x2r7UqEbD2lNWnqBWX4V7F+bkz+DSOSPU7ZmK4SUh8eu5RJIui/VaT1l5gndwllSvD9CVqW6QS5UIs1UF2VAOztkIcag7O7/d36ufvCxzz8vcHY29evzZ53t16gUbq1LVD7eZfzrZqxMXfUy4eD+/h+a+AWd4dX28wnlV1zTKti1D/D6ixPWSMsmHs0FDlbN7IR36V6dWD85XdNu28mUHPRS7sFLi4Lzm6uP18zYvacXuUq3YXaqSSfvlZF9rw6F/rd5kQfJnUDJpv2AOzpmkBrmiDEEunSkrT1DZV1/Vxq7VmViIi92RPRHiquObl+wZjeswdcne70BZmf62pe3ePx8AcoybOQIAgODkLMBs7lhUYVny6MIn1x6vv4+O3djung0n654NJ6v4qZpfebS+Svf+kxV26ljhM8in95+wtyNRGQUwupDrUShXWqqJH5yy188HgFzLyXVgJKn/pYv00YLuKnt3mSTJCgvleneTNF89m6/VQ6OfllSsx7a014sPx+aeDwz49OlUqe9fin8GfQ/X9t9t0antl+nxNtOV/BkcqHlBHJxrYnPHIrXO8Hhhp476+NyDNfPScepQWKxr1h5fZZBLHJw7tvgiuztbS6oT4tZcHWtwn9xmnFbsLsjKdGrLwSv0ZdWrIa60qf97r0PKvY0SN2ZN2Hy9fx+fTPf5yob20/wLWyZugpvwar+HvDr1VhT5cJPcmio4rLNXJ1+PrLpSLyhKd1rdqbMA02ibtGRX7Iu6V5Hp9g4va+HMebp4UewvWVFhmV7td7+iiujaNou1pnSnLv/obJX9uEAHrs6P4JL4DNK9f2nPZxBRRFFF034GoR2cq5I2yPY9XJIyB7kqhHRwzhTikgOcpGqHOADId3UWYNrfPk/Xz71QkvTT//u7vtN0g/oVl+nNY6dWWHfl7t0677fXqPUDYV9lNlXiM6jq/UuZP4OQDs7pZApyiRAnKWOQyyeVhbjUACepRiEOAPJZnU4huddjpw3e/8MhuuHqQr1z8pTyxz4v26mrVp2phS/3UJffLFLrHfkVXhLc6++kff9Sw/kMshXkQladEJca4CTlbYgDgJrKSQ+Me+NdHfpD6Sz1T3nkCx2q+Xk/h1j5+5cazGdQSZBLhDhJeR3kqhvi8jXAhShx1/DKrL6/q1e3ermO/5uZ36OTem+lVL9q94JXf+f8a7y61dTc/z+XevPWVLubp1w/yfzaivyTJTYM7+vViXvMJZzYeJ1XT9zUs8I2R++30qtTbxob+v3pQpKzJl4gU5CVlPdBrqoQl88BDgD2FQEGyKGGPhoJAHuLC9kBAIDgMAIDANXQrtmWXO9CRtbYvy7N1rdTTs4/yi9bp9y7aWsHv3+kVdb2bO8tmuzfmyh601yvfuOXd3v10LMHefWhTTd69bj2k7y6NOUOU73vu9qriz+veI+y0df429zRpnr3MUP2MQIDAACCQ4ABAADBIcAAAIDg0AMDANWw9Y8H+QvuzM1+VKZ01WqvPuil9v4KF9ThzmRJ6rVoevQe5dV/Puter57W5ZmMr/dB6XavHnarf+2bQyb6F4n8dMzx1dpP5AYjMAAAIDgEGAAAEBwCDAAACA49MABQDeZfMkSrS3d69cZTd3h1q6m1vEP7aLfz31Bkd452pAa6XvGqV9/6K/8K1r8Z3Nurdzb3f6PvP22xVx+wbd9vjNr5rA+8evuEfX5JVBMjMAAAIDgEGAAAEJyMU0izo9O4RjIAAKh3GIEBAADBoYkXAFJ8PPa4Cst2tY569Wkz/Bv/dft/m73aZX+3supf2/2bPXYYv+8NrXUtusNvnC55fIFfp65fw9c/6KGlFRf6175TUUGpV/uXykNtYgQGAAAEhwADAACCQ4ABAADBoQcGQIPnTujj1VOH311hnaOK/fqaT4/x6vcu93sh6ptIyu/VGyee79UHKLwemNoW3bI117uADBiBAQAAwSHAAACA4BBgAABAcOiBAdDgfTDSv+j4kcUVrxiSuiTq6teFyjdc7F+75vO+/s0aozW+CgpQvzECAwAAgkOAAQAAwSHAAACA4NADA6DBWXvl8V795ikTUtao+NX4wvZmXv3O9b29ukgLs7Jve+ubl/j3Abq5vV8/+NUhXt1h6hKv9jtmIEkqq/ip/G1L2xzsCNJhBAYAAASHAAMAAIJDgAEAAMGhBwZAg1PaxK8bW9VfhZfOGeHV3Z7Jbc9LTU1ZeYJXt/rq/RztSThcacX7W0384BSv7tjii7raHaRgBAYAAASHAAMAAIJDgAEAAMGhBwYAquHQv7pc74JnzdX+tWwmtxnn1St2F3h1yaT9an2fGoKWg1d49Zc52g8wAgMAAAJEgAEAAMEhwAAAgODQAwMA1bC5Y5FXt67l7RV26ujVH597sFfPvNTveelQWOzV16z1e2SKn3o9i3sH5B4jMAAAIDgEGAAAEBwCDAAACA49MABQDf0vXeTVHy3o7tVl7y7L+Hwr9L9uXd/DvXr777Z49ant/dd7vM30lFf0e14e29Leq198uL9XH6h5GfcPCA0jMAAAIDgEGAAAEBwCDAAACA49MAAanEbb/HrJLv8+R72KrMJzbu/wslcvnOn3lFy8aETGbRYVlnn1q/3u9+pIyu/JqKJevaZ0p1df/tHZXl32Y//eRweupucF+Y0RGAAAEBwCDAAACA4BBgAABMecc1WvBQB16LTI0Dr9YrL+3/Dqn/7f3yus852mG2p1H1J7YFbs9ntezvvtVV7d+oH5tbo/QH0xOzqtYlOaGIEBAAABIsAAAIDgEGAAAEBw6IEBUO/UdQ9MKjuqZ4VlK6/2L5v1zslTavSan5f5PS1XrTrTqxe+3MOru/zGv/dSdMeOGm0PyBf0wAAAgLxBgAEAAMEhwAAAgODQAwOg3sl1DwyA+oMeGAAAkDcIMAAAIDgEGAAAEBwCDAAACA4BBgAABIcAAwAAgkOAAQAAwSHAAACA4BBgAABAcAgwAAAgOAQYAAAQHAIMAAAIDgEGAAAEhwADAACCQ4ABAADBIcAAAIDgEGAAAEBwCDAAACA4BBgAABAcAgwAAAgOAQYAAASHAAMAAIJDgAEAAMEhwAAAgOAQYAAAQHAIMAAAIDgEGAAAEBwCDAAACA4BBgAABIcAAwAAgkOAAQAAwSHAAACA4BBgAABAcAgwAAAgOAQYAAAQHAIMAAAIDgEGAAAEhwADAACCQ4ABAADBIcAAAIDgEGAAAEBwCDAAACA4BBgAABAcAgwAAAgOAQYAAASHAAMAAIJDgAEAAMEhwAAAgOCYcy7X+wAAAFAjjMAAAIDgEGAAAEBwCDAAACA4BBgAABAcAgwAAAgOAQYAAASHAAMAAIJDgAEAAMEhwAAAgOAQYAAAQHAIMAAAIDgEGABA1pnZSWa2LNf7gfxFgAGABsTMPjKz7Wa2xczWmdlUM2uW7e045152znXP9uumMrMiM9uQ7j3E3+t6M2uatOwiM3sxC9v9jZn9ZV9fB3uPAAMADc+ZzrlmkvpK6ifphtQVLCaEY8TJkt5yzm2p5PECSZfX4f6gjoTwPycAoBY45z6R9E9JvSTJzF40s5vM7BVJ2yR1NrOWZnafma01s0/M7PdmVmBmxWa2ycx6JV7PzNrGR3famdk3zWx10mMdzGy6mX1mZh+a2WXx5Y3jz2kTr683s1IzaxGvf2dmd2R4G4MlPZXh8fGSrjaz/dI9aGY9zGy2mX1uZsvMbFh8eZGZvWVmo+N1gZm9YmY3mtnpkq6TdE58JGtx5k8atYEAAwANlJl9TbEAsChp8QhJF0tqLuljSVMllUrqKulISQMlXeSc2ynpcUk/THruMElznHPrU7YTkTRT0mJJB0k6VdIYMxvknNsh6XVJA+KrD4hv94Skek6GtzFY0j8yPL5Q0ouSrk59ID61NFvSw5LaSTpX0mQz+7pzbpek4ZLGmtnhkq5VbDTnJufc05JulvSIc66Zc653hu2jlhBgAKDhmWFmmyTNVSwc3Jz02FTn3LvOuVJJrRULCGOcc1vjweR2xQ70UuzAf27Sc38UX5aqv6S2zrmxzrldzrmVku5Neu4cSQPMrFDSEZLuiteN4899Kd2bMLMukgqdc1U1C98oabSZtU1ZPkTSR865B5xzpc65RZKmSxoqSc65JZJ+L2mGYgFohHOurIptoY4U5noHAAB17rvOuecqeWxV0p8PkdRI0lozSyyLJK3zgqQSMztG0jpJfSQ9keY1D5HUIR6aEgokvRz/8xxJtynWk/OOYqMi90k6VtIK59zGSvZ1sGJTYBk555aY2SzFRlHeS9mvY1L2q1DSQ0n1g5JukjTdOfd+VdtC3SHAAACSuaQ/r5K0U1Kb+IiMv6JzZWb2qGLTSOskzXLObU7zmqskfeicO6ySbc6T1F3S9xSbgvq3mXVULKBUNX10V1VvKO7Xkt6UNCFlv+Y4507L8LzJkmZJGmRmJzrn5saXuwzPQR1gCgkAkJZzbq2kZyVNMLMWZhYxsy5mNiBptYclnSPpx0o/fSRJr0nabGa/NLMm8YbYXmbWP76dbZLekDRKewLLPEkjVUmAMbMSSUcrNgpUnfeyQtIjki5LWjxLUjczG2FmjeL/9I/3vMjMRkg6StL58ec9mHS69jpJnQI5Uysv8cEDADI5T1KRpH9L+kLSY5IOTDzonFsgaaukDqpkOifeNzJEsSmmDyVtkDRFUsuk1eYoNl31WlLdXJX0v0j6lqT58Sbg6horqfyaMPHRooGK9eKskfSppFskFcdHgO6QdJ5zbotz7mHFGoJvjz99WvzfG83szRrsA7LEnGMUDAAQFjObLGmJc25yrvcFuUEPDAAgRG8pdmo2GihGYAAAQHDogQEAAMEhwAAAgODQAwOg3jktMpS5bQCSpNnRaZZuOSMwAAAgOAQYAAAQHAIMAAAIDgEGAAAEhwADAACCQ4ABAADBIcAAAIDgEGAAAEBwCDAAACA4BBgAABAcAgwAAAgOAQYAAASHAAMAAIJDgAEAAMEhwAAAgOAQYAAAQHAIMAAAIDgEGAAAEBwCDAAACA4BBgAABIcAAwAAgkOAAQAAwSHAAACA4BBgAABAcAgwAAAgOAQYAAAQHAIMAAAIDgEGAAAEhwADAACCQ4ABAADBIcAAAIDgEGAAAEBwCDAAACA4BBgAABAcAgwAAAgOAQYAAASHAAMAAIJDgAEAAMEhwAAAgOAQYAAAQHAIMAAAIDgEGAAAEBwCDAAACA4BBgAABIcAAwAAgkOAAQAAwSHAAACA4BBgAABAcAgwAAAgOAQYAAAQHAIMAAAIDgEGAAAEhwADAACCQ4ABAADBIcAAAIDgEGAAAEBwCDAAACA4BBgAABAcAgwAAAgOAQYAAASHAAMAAIJDgAEAAMEhwAAAgOAQYAAAQHAIMAAAIDgEGAAAEBwCDAAACE5hrncAABAGKy726uW39vHqJmsKvPqQSUu8uuyrr2pnx9AgMQIDAACCQ4ABAADBIcAAAIDg0AMD4P+3d+dBUlbnHsd/TzczOGxCzAiIXAcEWRIWL4JJIEpZ0RjiFiqi3piUllYkEUnK0uRegyZ6E0tLNCaYIQvEJOJGXMAltwSLK9cAV5AEAxpZAuayMyD7zsy5f3S/45ye7p4ZnJ7u0/39+Ac83W93n0bo+fV5znteoFk23j7Cq9eOfyzr8c/cUOnVd8/7qlcPvG+9V9fW1HyM0aHUMAMDAACCQ4ABAADBIcAAAIDgsAYGANAsdWUtO/7aTv6almvHT/fqb58/2qvXTjnPq8vmvd2yF0RJYQYGAAAEhwADAACCQ4ABAADBMedcvseAPDKzz0ua4ZwbkO+xAJGLY1fzwVSAYh06ePX6u4Z59f0TnvTqqzruadHzz5w2xZgAABQrSURBVD9c4dXTxoz16hPbtrfo+VAc5tf90dLdzgxMgTKzD8zssJkdMLPtZvY7M+vU2q/jnHuzLcKLmZWb2c507yH5XneYWccGt91sZm+0wuv+yMxmfdznAQAUFgJMYbvcOddJ0r9KOk/SlNQDLCGE/48XSFrhnDuQ4f64pO+04XgAAAEL4QdfyXPObZb0X5I+LUlm9oaZ/cTMFkk6JKmvmZ1qZjPNbKuZbTazH5tZ3Mzam9keM/t09HxmVpmc3TndzMaa2aYG951hZs+bWY2ZbTCzycnbT0k+5pPJ+gdmdsLMuiTr/zSzR7O8jXGS/pTl/ock3WFmXdPdaWYDzWy+mX1oZqvNbELy9nIzW2FmtyXruJktMrN7zOxSSXdJuiY5k/VO9j9pAEAo2AcmAGbWW4kA8EKDm78u6UuSVksySbMl7ZDUT1JHSa9I2uic+5WZvSDpOkk/SD52gqSFzrkdZja4wevEJL0saW7y+DMlvW5mq51zr5nZMkkXSno++es/JY1WIlxdKOmnWd7GOElXZrn/bUlvSLpDKTNNydbSfEn3JN/zEEnzzWyVc+49M7te0ptm9rqk8UrM5vzEOVdrZvdL6uecuz7LawNohrpDh7y6asoSr5459Vy/7u5fCyn2q4NePbf/q159ccVhr75z+mle3XuivzSqdvuOJkaMYsYMTGGbY2Z7JP1Z0kJJ9ze473fOuXedcyckfUKJgPBd59xB59wOJcLEtcljn2rwe0n6t+RtqUZKqnTO3eecO+acWy/pNw0eu1DShWbWTtJQST9P1qckH/s/6d6EmZ0tqZ1zbnUT7/ceSbeZWWXK7ZdJ+sA597hz7oRz7q9KhKirJck5t0rSjyXNUSIAfd05V9vEawEAAsYMTGG7yjn3eob7Njb4/VmSyiRtNatfrB1rcMx/S+pgZudL2i5puKQX0zznWZLOSIamSFzSm8nfL5T0iBJrclYqMSsyU9JnJK1zzu3KMNZxSszSZOWcW2Vmr0j6d0l/TxnX+SnjaifpiQb17yX9RNLzzrm1Tb0WACBsBJhwNZxL3SjpqKRPJmdk/AMTrZTZSrSFtkt6xTm3P81zbpS0wTnXP8NrLpY0QNJXlGhBvWdm/6JEQFmYZazjlJitaY4fSvqLpIdTxrXQOXdxlsdVK9E2+6KZjXHO/Tl5O6fjAkARIsAUAefcVjObJ+lhM7tb0gFJfSSd6ZyLgsVTSrRYdumjtTCplkrab2bfVyJwHJM0SFKFc26Zc+6QmS2XdKukLycfs1jSREk3pXtCM+sgaZQSs0DNeS/rzOxZSZOVmOWREsHkATP7uqRnkrcNl3TAOff35O0jJA2TdIWk35vZsOQZT9slXWxmMedcXXPGAODk1O7Z69+QUteO9e/uVz3Rq9dd+UuvXjHK3wFhxIRJXt19GmtgShlrYIrHNySVS3pP0m5Jz0nqGd3pnHtL0kFJZyhDOye5buQyJcLBBkk7Jc2QdGqDwxYq0a5a2qDurAzrXyRdJGmJc+5IC97LfUosRI7GtV/SJUqsxdkiaZukByW1T84APSrpG865A865p5RYEBwtKP5j8tddZvaXFowBAFDA2IkXOWVm1ZJWOeeq8z0WhIOdeEvDmupRXp06A5NqxAOpMzCLW31MKDyZduKlhYRcW6HEqdkAALQaAgxyyjn363yPAUBhGnz/Zq9e/iV/94MR5XGvrrxqo1drWk6GhUCwBgYAAASHAAMAAIKTtYXEQjoArSXTQjwAOBmsgQEA5MWJTf4amJrazilH+Ndemjvgea++uucV/vNt3dZqY0Pho4UEAACCQ4ABAADBIcAAAIDgtNkaGGvfXmumDpckVWyJ66xfrFLtvn1t9fIAAKCItFmA2Xj7CK0d/1h9/cwNlbp73lc18L71qq2paathAAAC1d447wQfoYUEAACC02YBpq7Mr6/tVKO146er96sHdPyS89pqGAAAoAi0WYCpmrpCgx+/VYMfv1VzDnatv7261yJ9r/oJtevRva2GUlCsfXtZ+/ZaO+18bfqPzynepUu+hwQAQMFrs4Zi3aFDqpqyRJI0c+q5mtm9UrFfHdTc/q/q4orDunP6aeo90al2+462GlJB2Hj7CEmqXx/E2iAAODnxbt28ets1A736uknzvPq73dZkfb4yize67birTXNkZkMW3eDVfW5c79V1Bw+26Pnwkbysgands1e1q9fp+Nit6jd3oiRpxahZ2jKhXz6Gk1d1ZX57jdYaAABNy/si3sH3b9byY4lE2+hS6SWgauqK+vYarbXGaK8BANLJe4A5sWlz/fUv5g54Xu169sjziNpW3aFD9e21maPO1eUXTdCVa78sSbq44rD+b/ppinc/Pc+jzJ/E6ffT9bdJj+lry97V2mnnK15Zme9hAQDyrKBOqi/1c/xr9+yV9uxV7VipX/VErbvyl1oxapZGTJik7tNKa21QJLW9du346fr2+aO1dsp5Kpv3dv4GBqDVxeWy3j/uff/ijQfG9PLqC+9a4tU/PP3nWZ+vronxHE8znLomH+V7Z/RvvXrIjJu9+uwbV/vPf+RIi56/lOV9Biadmomf1RdW7dcrm5frlc3L9dqWFXpty4r6+pXNy/XP2UMU69gx30PNmVJvrUVor2VGew1AKSuIABOl7nHvX6Fhf9qiJXf/XJO7va+65H/HXa2Ou9r6uk51emf0b/WPGf0UO+WUPI8+N0q9tRahvZYZ7TUApSxnPZvY0IHaOaKbfvSDx+tve+nDcyVJ898aqq7vxnT6sn3aMaqLLjhlqaR2eujs5zSgLHHa2qpjTtcu+aYkqc9jiYCz4YoOGjN2lX7Ze4EkaeUFM/SFcZPU4YW3cvU2CkKpt9Yk2mvp0F4DUMpy8pNxx6TPacH3p6pLzJ8dubTXosRvxi+SxjcexoCyuD6sParPz75D/e99V333r/Ae33extLWsXAOn3ar3L/+FJGnzWKn/C7l4F4Un3q2btl0zUNdNmuftX1BmcW9vgiGLblCfG9cX7f4Cg+/frOVfqtWI8niivTYt3yPKj6qpKzS4/FbdP+FJXdVxj6REe21+9V80bcxYndi2Pc8jRCGJDfX3RNk5wt8zpeGXzeaKvpRG5r811Ku7vutP8p++zL+A745Rfusz8WW2If9H1LqVZ3r1iw896tXRF+BMVh3zF7VEX5Izib48N7Thig5ePWbsKq+OvmBnsvKCGV79hXGTvLrYv5C3ppy0kKZMntUovDRHO8V1eryDvvfll7TrmR7a/Wp/7X61v/Zd9xntu+4zkiR3/JgqNpfGjETD1tqeMWdp+IJdjdprUYuN9lrpob0GoJTlJAlE3wYl6R8nDuvpPSO1+0QHzV0xXJL07EXTNaI8c1K+qcsm3TT82fp699DDkqSvPT1aktTniU3SxFyMPPfStdakxDeZTK21dSvP1IsPPZq2vSYlviXQXitdtNcAlKKCWMQLAADQEjn/+jq++k71enCxYp0r1HdkoiXyh6FjNOIM/3z9NcePqDLu1C1Wkesh5U2mtUFScn1QhrVBI0euYX1QE6L1QZK8NUKsD8r3iJAvOyZ9zqsXfH+qV59Mmz9V/brGyPjUuqXPmP1H0siR/rWLUte8fFh71Ks/P/sOr+5/77tenfo52hx9F/v11rJyrx447Vavjj6PM9k81q9L5TO7NeQkwMw52LW+jXTVdW9q2SVn6Ys9Vum2rgsbHRu3mM6ePVEDH9igo4N66UCvcu3tG9OR3sdlR2OKHY6pz0uJjX1i+msuhttmTnZt0JNVr0uK6+WD5+gTg3Zp1zM9JCXWfcRnnaYuT/+vpNJdH3RgTC9deNcSb9OqaKup487feOqd0b/VkBk36+wbVxflhlEfrQ86pLkDntfVPRMbf53Yui2/AwOAVpaTn3aP3TZBF874mbrFKnRv5TtS5TsZjx3zt/EacO9qndi9W/Ft23WqpFNzMagCkGltkCTNXTG8xWuDpMT6oGhtkMT6IKnxKfisDwKA4pOTT7jy197WqBdv19KvPJK2JbS77rCe23+OqmdcqZ6PLFbLLk5eHBq21iSp70hXsq01qfXaa5IatdhKvb0mpT8Fv9TaawCKS86+ovWf/JaumTNZH1xepva9D0iSjm7sJEmqevm42i1Yrp5anO0psool1x8PeniLTnz84baJTK01SY3aa81prUlSn5eOBN9ak1qvvSapvsVGe+2K+mvFNGyxNae9JnFNltBNmTzLq1tjzUu+Jf69N+TPWL988Byvjj4TIonPhob8Oj7rNK+OPj+yccePeXWpfM4Ugpz+SbdbsFz9su/pc1Lc/gP69d4q3Xzqeqku+8W/CgmttcxS22uSvNPvaa8lFiin2926qfaapIw7XGdqr0lsqAWgsAUZFa1zp0R4CQytteYZX32nJHlnr9FeS5xBknZ36ybaa5IynsGWqb0mlVaLDUB4ggswsc6dVfHEIUnSAzuHye3d18QjCku61pqUaK+1RmtNKo72mqSMZ69la69J8s5eo72WCDCpZ7CVansNQPEI7lPryOiBeqrqZ5LK9OLMseq+7+P9sM+HXLXWpCJqr0kZW2yl3F6TGu9u3VR7TUq/u3WxtNeQXurfm6ZErdvI03tGNjomOmsyEu2uHnn2oule3dTfy9Z2U5dNfp3SVm5KtOt7pOG/kebq84Q/Bv5d5U5wO/HuHFKmDrHEZXi7rT7WxNGlJ/T22u66w2nv3113WL/Z21vnPjxJHS9dr9rdu9t4hIVjfPWdWjysXKvHVqjvk05/2Dmm0TFrjh/J+GcJAMUguBmYunZSrXP61PxvacAbKxXOHEPuFVN7TZJ39hrttY++TWfbHDJde01S2s0hi6G9BqB0BRVgjlw2Sk9+86daerRC59y4nPCSgvZadqG31yQ1eQZbuvaaVNwtNgClKagAE/vOdg0qj2no47eoSkuafkCJob2WXejtNUlpz2Dj7DW0hujsv0ivBxt/AYo23oxE17eL/GGo385MPXOwpdYc9/ciqoz7r1fsZyAiu+DWwAAAAAQxAxOvrJQkXdrjPX12+fWqumdpnkdUmFgflFkxrA+SlHZ361ysD5IUzBohAKUpiABTW1MjSVowpKMqtTrPoylMrA/KrhjWB0nsbg0AkSACDJrG+qDsWB+UXajrg5DZnINdvbqpfWGiDSQjyy45q9Ex0bXbIqlnwbVU3PxVDGfP9jdNGfjABq8+OqiXV0dn2UX29vWfL9rcMmJH/fuja8pF+rzkr7nhTL3CxhqYIhCvrKS91oSovTZw3kS1f2NlvodTUNK110JrsQEoPczAFIHamhraa1nQXsuuWNprAEoLMzAoelF77Zanb8n3UAoS7TUAIWIGBkWN9lrTOHutOD122wSvjjZDjKTuoXJv6uaIGTZLbE1j/uZdRl0D7vVnkU+kXDIkvm27V6du0MiGjaWFAIOiRnstO9prAEJFCwkoYbTXAISKAAOUKNprAEJGCwkoUbTXilv5a297dXQ9rcjSrzzi1a1xXaHddYe9+rn953h19YwrvbrnI/4Zb1zHCy3BDAwAAAgOAQYAAASHAAMAAILDGhgAKAHRFc0j18yZ7NUfXF7m1dEVz7M5urGTV1e97F97qN2C5V79ca+YHqJYE/ME0dXfI1wFvvmYgQEAAMEhwAAAgOAQYAAAQHBYAwMAJSh1fUq/BXkaSJFx+/21Q7/eW+XVN5+63n9AHRfwOFnMwAAAgOAQYAAAQHAIMAAAIDisgQEAoJVYZ39vnEZrXtBqmIEBAADBIcAAAIDgEGAAAEBwWAMDAMBJinXu7NUVTxzKevwDO4d5tdu7r9XHVCqYgQEAAMEhwAAAgOAQYAAAQHBYAwMAwEk6MnqgVz9V9bOUI8q86sWZY726+77FORhVaWAGBgAABIcAAwAAgpO1hTS/7o/WVgMBAABoLmZgAABAcFjECwDASdo5xF+k2yFWluHIhG6rj+VyOCWFGRgAABAcAgwAAAgOAQYAAASHNTAAAJykupSforXOefWn5n/Lqwe8sdKr/aPREszAAACA4BBgAABAcAgwAAAgOKyBAQCgmY5cNsqrn/zmT7166dEKrz7nxuVezZqX1sMMDAAACA4BBgAABIcAAwAAgsMaGAAAmin2ne1ePajcnwcY+vgtXl2lJTkfU6liBgYAAASHAAMAAIJDgAEAAMFhDQwAAGnEKysb3XZpj/e8+rPLr/fqqnuW5nRM+AgzMAAAIDgEGAAAEBwCDAAACA5rYAAASKO2pqbRbQuGdPTqSq1uq+EgBTMwAAAgOAQYAAAQHAIMAAAIDgEGAAAEhwADAACCQ4ABAADBIcAAAIDgEGAAAEBwCDAAACA4BBgAABAcAgwAAAgOAQYAAASHAAMAAIJDgAEAAMEhwAAAgOAQYAAAQHAIMAAAIDgEGAAAEBwCDAAACA4BBgAABIcAAwAAgkOAAQAAwSHAAACA4BBgAABAcAgwAAAgOAQYAAAQHAIMAAAIDgEGAAAEhwADAACCQ4ABAADBIcAAAIDgEGAAAEBwCDAAACA4BBgAABAcAgwAAAgOAQYAAASHAAMAAIJDgAEAAMEhwAAAgOAQYAAAQHAIMAAAIDgEGAAAEBwCDAAACA4BBgAABIcAAwAAgkOAAQAAwSHAAACA4BBgAABAcAgwAAAgOAQYAAAQHAIMAAAIDgEGAAAEhwADAACCQ4ABAADBIcAAAIDgEGAAAEBwCDAAACA4BBgAABAcAgwAAAgOAQYAAASHAAMAAIJDgAEAAMEhwAAAgOAQYAAAQHAIMAAAIDgEGAAAEBwCDAAACA4BBgAABIcAAwAAgkOAAQAAwSHAAACA4BBgAABAcMw5l+8xAAAAtAgzMAAAIDgEGAAAEBwCDAAACA4BBgAABIcAAwAAgkOAAQAAwfl/ryHyVLLqALsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x1440 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "@Time    :2020/1/1 16:06\n",
    "@Author  : 梁家熙\n",
    "@Email:  :11849322@mail.sustech.edu.cn\n",
    "\"\"\"\n",
    "import json\n",
    "from torch.utils.data._utils.collate import default_collate\n",
    "from tqdm import tqdm\n",
    "import random\n",
    "from pprint import pprint\n",
    "import os\n",
    "from itertools import chain\n",
    "import collections\n",
    "from typing import List, Dict, Tuple\n",
    "import logging\n",
    "from collections import Iterable\n",
    "logger = logging.getLogger(__name__)\n",
    "logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n",
    "from fastai import *\n",
    "from fastai.vision import *\n",
    "\n",
    "\n",
    "class ConsImage(ItemBase):\n",
    "    def __init__(self, imgs_x: List[Image], imgs_y: List[Image]):\n",
    "        assert isinstance(imgs_x[0], Image)\n",
    "        self.imgs_x = imgs_x\n",
    "        self.imgs_y = imgs_y\n",
    "\n",
    "    def apply_tfms(self, tfms:Collection, **kwargs):\n",
    "        for img in chain(self.imgs_x, self.imgs_y):\n",
    "            img.apply_tfms(tfms, **kwargs)\n",
    "\n",
    "    def show(self, ax:plt.Axes, **kwargs):\n",
    "        raise NotImplemented\n",
    "    def __str__(self):\n",
    "        return f\"{len(self.imgs_x)} {len(self.imgs_y)}\"\n",
    "\n",
    "\n",
    "\n",
    "def generate(data: Tensor, winsize_x, winsize_y):\n",
    "    assert winsize_x +  winsize_y <= data.shape[0]\n",
    "    winsize = winsize_x + winsize_y\n",
    "    for d in data.permute(1,0,2,3):\n",
    "        assert len(d.shape) == 3\n",
    "        for i in range(d.shape[0] - winsize + 1):\n",
    "            yield d[i: i+winsize_x, :, :], d[i+winsize_x: i+winsize_x+winsize_y, :, :]\n",
    "class TMPImageList(ItemList):\n",
    "    def reconstruct(self, t:Tensor, x:Tensor=None):\n",
    "        return Image(torch.cat([w for w in t], -1).unsqueeze(0))\n",
    "class MyImageList(ItemList):\n",
    "    _bunch = DataBunch\n",
    "    _label_cls = TMPImageList\n",
    "\n",
    "    @classmethod\n",
    "    def from_npy(cls, file: Path, winsize_x = 3, winsize_y = 3):\n",
    "        # create ItemList, which contains objects we want\n",
    "        data = np.load(file)\n",
    "        data = data/255\n",
    "        if not isinstance(data, Tensor):\n",
    "            data = torch.from_numpy(data)\n",
    "        assert len(data.shape) == 4 # some tensor like: 20, 50, 64, 64\n",
    "        items = []\n",
    "        for x, y in tqdm(generate(data, winsize_x, winsize_y), desc=\"from npy\"):\n",
    "            x = [Image(w.unsqueeze(0)) for w in x]\n",
    "            y = [Image(w.unsqueeze(0)) for w in y]\n",
    "            items.append(ConsImage(x, y))\n",
    "        res = cls(items)\n",
    "        return res\n",
    "\n",
    "    def reconstruct(self, t:Tensor, x:Tensor=None):\n",
    "        return Image(torch.cat([w for w in t], -1).unsqueeze(0))\n",
    "\n",
    "    # def get(self, i)->Any:\n",
    "    #     \"Subclass if you want to customize how to create item `i` from `self.items`.\"\n",
    "    #     return self.items[i]\n",
    "    def show_xys(self, xs, ys, imgsize:int=4, figsize:Optional[Tuple[int,int]]=None, **kwargs):\n",
    "        \"Show the `xs` (inputs) and `ys` (targets) on a figure of `figsize`.\"\n",
    "        rows = len(xs)\n",
    "\n",
    "        axs = subplots(rows, 2, imgsize=imgsize, figsize=figsize)\n",
    "        for i in range(0, len(axs.flatten()),  2):\n",
    "            ax = axs.flatten()[i]\n",
    "            index = i // 2\n",
    "            x = xs[index]\n",
    "            y = ys[index]\n",
    "            x.show(ax = ax, **kwargs)\n",
    "            y.show(ax = axs.flatten()[i+1], **kwargs)\n",
    "#         for ax in axs.flatten()[len(xs):]: ax.axis('off')\n",
    "        plt.tight_layout()\n",
    "\n",
    "    def show_xyzs(self, xs, ys, zs, imgsize:int=4, figsize:Optional[Tuple[int,int]]=None, **kwargs):\n",
    "        \"Show `xs` (inputs), `ys` (targets) and `zs` (predictions) on a figure of `figsize`.\"\n",
    "        if self._square_show_res:\n",
    "            title = 'Ground truth\\nPredictions'\n",
    "            rows = int(np.ceil(math.sqrt(len(xs))))\n",
    "            axs = subplots(rows, rows, imgsize=imgsize, figsize=figsize, title=title, weight='bold', size=12)\n",
    "            for x,y,z,ax in zip(xs,ys,zs,axs.flatten()): x.show(ax=ax, title=f'{str(y)}\\n{str(z)}', **kwargs)\n",
    "            for ax in axs.flatten()[len(xs):]: ax.axis('off')\n",
    "        else:\n",
    "            title = 'Ground truth/Predictions'\n",
    "            axs = subplots(len(xs), 2, imgsize=imgsize, figsize=figsize, title=title, weight='bold', size=14)\n",
    "            for i,(x,y,z) in enumerate(zip(xs,ys,zs)):\n",
    "                x.show(ax=axs[i,0], y=y, **kwargs)\n",
    "                x.show(ax=axs[i,1], y=z, **kwargs)\n",
    "\n",
    "def label_func(input):\n",
    "    # print(type(x))\n",
    "    # print(x)\n",
    "    return input.imgs_y\n",
    "\n",
    "def collate(samples):\n",
    "    x, y = zip(*samples)\n",
    "    tensor_x = torch.stack([torch.cat([w.data for w in c.imgs_x]) for c in x])\n",
    "    tensor_y = torch.stack([torch.cat([w.data for w in c.imgs_y]) for c in x])\n",
    "    assert len(tensor_x.shape) == len(tensor_y.shape) == 4\n",
    "    return tensor_x, tensor_y\n",
    "\n",
    "BATCHSIZE = 32\n",
    "WIN_ENCODER = 4\n",
    "WIN_DECODER = 1\n",
    "\n",
    "# data_pth = Path('./data_samples.npy')\n",
    "data_pth = Path('./mnist_test_seq.npy')\n",
    "data = MyImageList.from_npy(data_pth, winsize_x=WIN_ENCODER, winsize_y=WIN_DECODER) # construct MyImageList which contains itembase\n",
    "data = data.split_by_rand_pct(0.15) # ItemLists which contains trains and valids\n",
    "data = data.label_from_func(label_func)\n",
    "data = data.databunch(bs = BATCHSIZE, collate_fn = collate, num_workers = 0) # to debug, we set\n",
    "\n",
    "data.show_batch(title = \"Preview / Next\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class EncoderDecoder(torch.nn.Module):\n",
    "    def __init__(self, encoder):\n",
    "        super(EncoderDecoder, self).__init__()\n",
    "        self.encoder = encoder\n",
    "\n",
    "\n",
    "from convlstm import *\n",
    "def get_model():\n",
    "    encoder = ConvLSTM(input_size=(64, 64),\n",
    "                 input_dim=1,\n",
    "                 hidden_dim=[64, 64, 1],\n",
    "                 kernel_size=(3, 3),\n",
    "                 num_layers=3,\n",
    "                 batch_first=True,\n",
    "                 bias=True,\n",
    "                 return_all_layers=True)\n",
    "\n",
    "    decoder = ConvLSTMDeocder(input_size=(64, 64),\n",
    "                 input_dim=1,\n",
    "                 hidden_dim=[64, 64, 1],\n",
    "                 kernel_size=(3, 3),\n",
    "                 num_layers=3,\n",
    "                       decode_step = WIN_DECODER,\n",
    "                 batch_first=True,\n",
    "                 bias=True,\n",
    "                 return_all_layers=False)\n",
    "    model = EncoderDecoder(encoder, decoder)\n",
    "    return model\n",
    "\n",
    "def loss_func(x, y):\n",
    "    x = x.view(-1).float()\n",
    "    y = y.view(-1).float()\n",
    "\n",
    "    assert len(x) == len(y)\n",
    "    return F.l1_loss(x, y)\n",
    "\n",
    "model = get_model()\n",
    "learner = Learner(data, model, loss_func = loss_func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
     ]
    }
   ],
   "source": [
    "learner.lr_find()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min numerical gradient: 7.59E-05\n",
      "Min loss divided by 10: 2.51E-04\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learner.recorder.plot(suggestion = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EncoderDecoder\n",
       "======================================================================\n",
       "Layer (type)         Output Shape         Param #    Trainable \n",
       "======================================================================\n",
       "Conv2d               [256, 64, 64]        150,016    True      \n",
       "______________________________________________________________________\n",
       "Conv2d               [256, 64, 64]        295,168    True      \n",
       "______________________________________________________________________\n",
       "Conv2d               [4, 64, 64]          2,344      True      \n",
       "______________________________________________________________________\n",
       "Conv2d               [256, 64, 64]        150,016    True      \n",
       "______________________________________________________________________\n",
       "Conv2d               [256, 64, 64]        295,168    True      \n",
       "______________________________________________________________________\n",
       "Conv2d               [4, 64, 64]          2,344      True      \n",
       "______________________________________________________________________\n",
       "\n",
       "Total params: 895,056\n",
       "Total trainable params: 895,056\n",
       "Total non-trainable params: 0\n",
       "Optimized with 'torch.optim.adam.Adam', betas=(0.9, 0.99)\n",
       "Using true weight decay as discussed in https://www.fast.ai/2018/07/02/adam-weight-decay/ \n",
       "Loss function : function\n",
       "======================================================================\n",
       "Callbacks functions applied "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learner.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x1440 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learner.show_results()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.022427</td>\n",
       "      <td>0.022446</td>\n",
       "      <td>2:43:37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learner.fit_one_cycle(1, 1e-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learner.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "learner.save('stage_2h43mins')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x1440 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learner.show_results()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "from npy: 130000it [00:10, 12616.88it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x1440 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "BATCHSIZE = 32\n",
    "WIN_ENCODER = 4\n",
    "WIN_DECODER = 4\n",
    "\n",
    "# data_pth = Path('./data_samples.npy')\n",
    "data_pth = Path('./mnist_test_seq.npy')\n",
    "data = MyImageList.from_npy(data_pth, winsize_x=WIN_ENCODER, winsize_y=WIN_DECODER) # construct MyImageList which contains itembase\n",
    "data = data.split_by_rand_pct(0.15) # ItemLists which contains trains and valids\n",
    "data = data.label_from_func(label_func)\n",
    "data = data.databunch(bs = BATCHSIZE, collate_fn = collate) \n",
    "\n",
    "data.show_batch(title = \"Preview / Next\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = get_model()\n",
    "learner = Learner(data, model, loss_func = loss_func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learner.model.decoder.decode_step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Learner(data=DataBunch;\n",
       "\n",
       "Train: LabelList (110500 items)\n",
       "x: MyImageList\n",
       "4 4,4 4,4 4,4 4,4 4\n",
       "y: TMPImageList\n",
       "[Image (1, 64, 64) Image (1, 64, 64) Image (1, 64, 64) Image (1, 64, 64)],[Image (1, 64, 64) Image (1, 64, 64) Image (1, 64, 64) Image (1, 64, 64)],[Image (1, 64, 64) Image (1, 64, 64) Image (1, 64, 64) Image (1, 64, 64)],[Image (1, 64, 64) Image (1, 64, 64) Image (1, 64, 64) Image (1, 64, 64)],[Image (1, 64, 64) Image (1, 64, 64) Image (1, 64, 64) Image (1, 64, 64)]\n",
       "Path: .;\n",
       "\n",
       "Valid: LabelList (19500 items)\n",
       "x: MyImageList\n",
       "4 4,4 4,4 4,4 4,4 4\n",
       "y: TMPImageList\n",
       "[Image (1, 64, 64) Image (1, 64, 64) Image (1, 64, 64) Image (1, 64, 64)],[Image (1, 64, 64) Image (1, 64, 64) Image (1, 64, 64) Image (1, 64, 64)],[Image (1, 64, 64) Image (1, 64, 64) Image (1, 64, 64) Image (1, 64, 64)],[Image (1, 64, 64) Image (1, 64, 64) Image (1, 64, 64) Image (1, 64, 64)],[Image (1, 64, 64) Image (1, 64, 64) Image (1, 64, 64) Image (1, 64, 64)]\n",
       "Path: .;\n",
       "\n",
       "Test: None, model=EncoderDecoder(\n",
       "  (encoder): ConvLSTM(\n",
       "    (cell_list): ModuleList(\n",
       "      (0): ConvLSTMCell(\n",
       "        (conv): Conv2d(65, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      )\n",
       "      (1): ConvLSTMCell(\n",
       "        (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      )\n",
       "      (2): ConvLSTMCell(\n",
       "        (conv): Conv2d(65, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (decoder): ConvLSTMDeocder(\n",
       "    (cell_list): ModuleList(\n",
       "      (0): ConvLSTMCell(\n",
       "        (conv): Conv2d(65, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      )\n",
       "      (1): ConvLSTMCell(\n",
       "        (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      )\n",
       "      (2): ConvLSTMCell(\n",
       "        (conv): Conv2d(65, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "), opt_func=functools.partial(<class 'torch.optim.adam.Adam'>, betas=(0.9, 0.99)), loss_func=<function loss_func at 0x7f382002b6a8>, metrics=[], true_wd=True, bn_wd=True, wd=0.01, train_bn=True, path=PosixPath('.'), model_dir='models', callback_fns=[functools.partial(<class 'fastai.basic_train.Recorder'>, add_time=True, silent=False)], callbacks=[], layer_groups=[Sequential(\n",
       "  (0): Conv2d(65, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (1): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (2): Conv2d(65, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (3): Conv2d(65, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (4): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (5): Conv2d(65, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       ")], add_time=True, silent=False)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learner.load('stage_2h43mins')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.034675</td>\n",
       "      <td>0.034542</td>\n",
       "      <td>3:30:03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learner.show_resultsone_cycle(1, 1e-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x1440 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learner.show_results()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "liangjiaxi",
   "language": "python",
   "name": "liangjiaxi"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
